CN116258496A

CN116258496A - Payment processing method, device, equipment, medium and program product

Info

Publication number: CN116258496A
Application number: CN202111494326.0A
Authority: CN
Inventors: 王少鸣; 郭润增; 陈南瑾
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-12-08
Filing date: 2021-12-08
Publication date: 2023-06-13

Abstract

The embodiment of the application discloses a payment processing method, a device, equipment, a medium and a program product, wherein the method comprises the following steps: in response to a bio-payment request for a target order, collecting biometric characteristics of at least two payment objects; determining a target payment object from the at least two payment objects; acquiring object information of a target payment object according to biological characteristics of the target payment object; and carrying out payment processing on the target order based on the object information of the target payment object. By adopting the embodiment of the application, the target payment object can be effectively determined from at least two payment objects in the payment scene.

Description

Payment processing method, device, equipment, medium and program product

Technical Field

The present application relates to the field of computer technology, and in particular to the field of artificial intelligence, and more particularly to a payment processing method, a payment processing apparatus, a payment processing device, a computer readable storage medium and a computer program product.

Background

With the rapid development of internet technology, payment methods for making pay-per-line based on the biometric features of users are widely used. For example, a line-down face brush is performed based on the face features of the user to effect purchase of the merchandise. The research shows that the offline face-brushing payment only supports single face-brushing payment, and how to effectively determine users who pay a target order from a plurality of users becomes a research topic.

Disclosure of Invention

The embodiment of the application provides a payment processing method, a device, equipment, a medium and a program product, which can effectively determine a target payment object from at least two payment objects in a payment scene to carry out payment processing on a target order.

In one aspect, an embodiment of the present application provides a payment processing method, including:

in response to a bio-payment request for a target order, collecting biometric characteristics of at least two payment objects;

determining a target payment object from the at least two payment objects;

acquiring object information of a target payment object according to biological characteristics of the target payment object;

and carrying out payment processing on the target order based on the object information of the target payment object.

In another aspect, an embodiment of the present application provides a payment processing apparatus, including:

the system comprises an acquisition unit, a target order generation unit and a payment unit, wherein the acquisition unit is used for responding to a biological payment request for the target order and acquiring biological characteristics of at least two payment objects;

a processing unit for determining a target payment object from at least two payment objects;

the processing unit is also used for acquiring object information of the target payment object according to the biological characteristics of the target payment object;

And the processing unit is also used for carrying out payment processing on the target order based on the object information of the target payment object.

In one implementation, the at least two payment objects include a first payment object and a second payment object, and the collection unit is configured to, when collecting the biometric features of the at least two payment objects, specifically:

sequentially collecting the biological characteristics of the first payment object and the biological characteristics of the second payment object in a set time range; or alternatively, the process may be performed,

and acquiring a biological characteristic snap image containing the first payment object and the second payment object, and dividing the biological characteristic of the first payment object and the biological characteristic of the second payment object from the biological characteristic snap image.

In one implementation, the processing unit is configured to, when determining the target payment object from at least two payment objects, specifically:

outputting a payment object selection list, wherein the payment object selection list comprises the collected biological characteristics of each payment object in at least two payment objects;

in response to a selection operation of the payment object selection list, the selected payment object is determined as a target payment object.

Outputting a biometric of each of the at least two payment objects;

if a confirmation operation of at least one payment object in the at least two payment objects is detected, scoring the biological characteristics of each payment object in the at least one payment object according to a payment strategy to obtain the score of each payment object;

a target payment object is selected from the at least one payment object based on the score of each payment object.

In one implementation, the payment policy includes a visual recognition algorithm, and the processing unit is configured to score, according to the payment policy, a biometric feature of each payment object in the at least one payment object, and when obtaining a score of each payment object, the processing unit is specifically configured to:

and scoring the biological characteristics of each payment object in the at least one payment object by adopting a visual recognition algorithm to obtain the visual recognition score of each payment object.

In one implementation, the payment policy includes a behavior prediction rule, and the processing unit is configured to score, according to the payment policy, a biometric feature of each payment object in the at least one payment object, and when obtaining a score of each payment object, specifically configured to:

Performing feature recognition processing on the biological characteristics of each payment object in at least one payment object to obtain the identification of each payment object;

acquiring behavior data corresponding to each payment object according to the identification of each payment object;

scoring the behavior data corresponding to each payment object according to the behavior prediction rule to obtain a behavior prediction value of each payment object;

wherein the behavior data comprises: payment times, payment electronic resource amount, payment field.

In one implementation, the payment policy includes a look-alike rule, and the processing unit is configured to score, according to the payment policy, a biological feature of each payment object in the at least one payment object, and when obtaining a score of each payment object, the processing unit is specifically configured to:

acquiring a reference biological characteristic of a reference object;

calculating the appearance similarity between the biological characteristics of each payment object in the at least one payment object and the reference biological characteristics according to the appearance similarity rule;

and obtaining a similarity score of the appearance of each payment object based on the similarity of the appearance between the biological characteristics of each payment object and the reference biological characteristics.

In one implementation, the processing unit is configured to, when selecting a target payment object from at least one payment object based on the score of each payment object, specifically:

Selecting one or more payment objects as target payment objects according to the order of the scores from high to low;

alternatively, one or more payment objects with scores greater than a score threshold are targeted payment objects.

In one implementation, the processing unit is further configured to:

outputting a payment object confirmation interface, wherein the payment object confirmation interface comprises object information of a target payment object;

triggering and executing the step of carrying out payment processing on the target order based on the object information of the target payment object in response to a payment confirmation event of the payment object confirmation interface;

wherein the payment confirmation event comprises: an event generated when a confirmation operation is performed with respect to the object information of the target payment object, an event generated when the display time of the object information of the target payment object in the payment object confirmation interface is longer than a time length threshold.

In one implementation, the processing unit is further configured to:

displaying a score list in a payment object confirmation interface, wherein the score list comprises the score of each payment object;

when the target score in the score list is selected, taking the payment object corresponding to the target score as a target payment object, and displaying object information of the payment object corresponding to the target score in a payment object confirmation interface.

a processor adapted to execute a computer program;

a computer readable storage medium having a computer program stored therein, which when executed by a processor, implements the payment processing method described above.

In another aspect, embodiments of the present application provide a computer readable storage medium storing a computer program adapted to be loaded by a processor and to perform the above-described payment processing method.

In another aspect, embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the payment processing apparatus reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the payment processing apparatus performs the above-described payment processing method.

In the embodiment of the application, in response to a biological payment request for a target order, biological characteristics of at least two payment objects can be acquired; and supporting determination of a target payment object from the at least two payment objects such that payment processing of the target order may be performed using object information of the target payment object. In the above scheme, the method supports the collection of biological characteristics of a plurality of users in the payment process, such as the biological characteristics are face images, and determines a target payment object based on the biological characteristics of the plurality of users to pay the target order; compared with the payment of the target order based on the biological characteristics of a single user, the method and the device can realize the payment of the target order by at least two users in a payment scene, enrich the biological payment form and further facilitate the popularization and propagation of the biological payment.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1a illustrates a schematic architecture of a payment processing system provided in one exemplary embodiment of the present application;

FIG. 1b illustrates a schematic diagram of a payment processing system provided in accordance with an exemplary embodiment of the present application;

FIG. 2 illustrates a flow diagram of a payment processing method provided in an exemplary embodiment of the present application;

fig. 3a shows a schematic diagram of acquiring face images of a first user and a second user by a 3D camera invoking a payment terminal according to an exemplary embodiment of the present application;

fig. 3b shows a schematic diagram of acquiring face images of a first user and a second user by using a 3D camera of a payment terminal according to an exemplary embodiment of the present application;

FIG. 4a illustrates a schematic diagram of a determination of a payment object by a user from at least two users provided in an exemplary embodiment of the present application;

FIG. 4b is a schematic diagram of determining a payment object from at least two users based on a payment policy according to an exemplary embodiment of the present application;

FIG. 4c illustrates a schematic diagram of a payment object selection list provided by an exemplary embodiment of the present application;

FIG. 5 illustrates a schematic diagram of a payment object validation interface provided by an exemplary embodiment of the present application;

fig. 6 is a schematic diagram showing output of a payment processing result in a payment terminal and an intelligent terminal used by a payment object according to an exemplary embodiment of the present application;

FIG. 7 is a flow chart of a payment processing method according to an exemplary embodiment of the present application;

FIG. 8 is a schematic diagram of a face image participation scoring process for selecting at least one user from at least two users according to an exemplary embodiment of the present application;

FIG. 9a is a schematic diagram of scoring face images of at least two users according to a visual recognition algorithm according to an exemplary embodiment of the present application;

FIG. 9b is a schematic diagram of scoring face images of at least two users according to behavior prediction rules according to an exemplary embodiment of the present application;

FIG. 9c is a schematic diagram of scoring face images of at least two users according to a look-like rule according to an exemplary embodiment of the present application;

FIG. 10 illustrates a schematic diagram of a policy identification of an output payment policy provided by an exemplary embodiment of the present application;

FIG. 11a is a schematic diagram of outputting account information of a payment object according to an exemplary embodiment of the present application;

FIG. 11b is a diagram illustrating the output of account information for a payment object according to one exemplary embodiment of the present application;

fig. 12 is a schematic diagram showing a structure of a payment processing apparatus according to an exemplary embodiment of the present application;

fig. 13 is a schematic structural diagram of a supporting processing device according to an exemplary embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

The embodiment of the application provides a payment processing scheme, which relates to the related technology in the field of artificial intelligence, wherein:

1) Artificial intelligence (Artificial Intelligence, AI).

Artificial intelligence is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and expand human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision. The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, mass payment processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

2) The embodiment of the application particularly relates to the directions of computer vision technology and the like in the field of artificial intelligence. The Computer Vision technology (CV) is a science for researching how to make a machine "look at", and more specifically, a camera and a Computer are used to replace human eyes to perform machine Vision such as identifying, tracking and measuring on a target, and further perform graphic processing, so that the Computer is processed into an image more suitable for human eyes to observe or transmit to an instrument to detect. As a scientific discipline, computer vision research-related theory and technology has attempted to build artificial intelligence systems that can acquire information from images or multidimensional data. Computer vision techniques typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content/behavior recognition, three-dimensional object reconstruction, 3D techniques, virtual reality, augmented reality, synchronous positioning, and map construction, among others.

3) Computer vision techniques also include biometric (BIOMET RICS) techniques such as face recognition, fingerprint recognition, and the like. Biometric identification refers to a technique of personal identification by a computer using physiological characteristics (such as fingerprint, iris, face, DNA, etc.) or behavioral characteristics (gait, keystroke habit, etc.) inherent to a human body. Among them, iris recognition is a biometric technique for performing identification based on the iris in the eyes of a user. Specifically, the eye structure of a human is composed of a sclera, an iris, a pupil, a lens, a retina and the like; the iris is an annular portion between the black pupil and the white sclera, which contains many interlaced spots, filaments, crowns, fringes, crypts, etc. of detail; these features determine the uniqueness of the iris features and also determine the uniqueness of the identification of the iris; therefore, iris recognition can be widely applied to various fields such as a payment field, a public security field, and the like.

Face recognition is a biological feature recognition technology for performing identity recognition based on facial feature information of a person. Specifically, the camera is used for collecting images or video streams containing faces (namely, the images comprise a plurality of image frames), and face recognition is carried out on faces detected in the images, so that face image can be used for exchanging face characteristic information, and the effect of identity recognition is achieved. The camera can be a 3D camera (such as a depth camera, an infrared camera and the like), and the 3D camera is similar to a traditional camera, and is added with relevant living body software and hardware, so that the safety of information (such as images) can be ensured. Along with the rapid development of artificial intelligence technology, face recognition is widely applied to various fields such as traffic, business, public safety and the like by virtue of non-contact (i.e. user noninductivity) thereof; for example, various travel modes such as aviation, high-speed rail, road passenger transport and the like adopt face recognition to realize consistency of noninductive detection people, tickets and certificates; as another example, many terminals or applications employ face recognition to effect unlocking of the terminal or application; for another example, the application program adopts face recognition to realize the payment or login function; etc.

The embodiment of the application proposes a payment processing scheme based on the above-described biometric identification technology, the payment processing scheme supports collecting biometric features (such as face images) of a plurality of payment objects (i.e. users with consumption requirements) in a payment scene, and determining a target payment object from the biometric features of the plurality of payment objects to pay for a target order; compared with the payment of the target order based on the biological characteristics of a single payment object, the method realizes a plurality of user payment modes in a payment scene, enriches the payment modes of biological payment, and is further beneficial to popularization and propagation of biological payment. For convenience of description, the following embodiments of the present application will take biological features as face features (or referred to as face images), and payment objects may be simply referred to as users as examples.

The payment processing scheme according to the embodiment of the present application will be described below in conjunction with an actual payment processing scenario. Referring to fig. 1a, fig. 1a illustrates a schematic architecture diagram of a payment processing system according to an exemplary embodiment of the present application; as shown in fig. 1a, the payment processing system may include, but is not limited to: a payment terminal 101 and a backend service device 102. The naming and the number of the payment terminal and the back-end service equipment are not limited; for example: the payment terminal 101 may be referred to as an operation terminal, a merchant terminal, an intelligent terminal, or the like; the backend service device 102 may be referred to as a backend service device, backend server, or the like.

The payment terminal 101 is a device with a function of collecting biological characteristics, such as a device with a function of collecting facial images of a user; the device may be a face brushing device used by a merchant, for example: the merchant uses face brushing equipment to collect face images of payment objects during offline payment; alternatively, the device may be a terminal used by a consumer having a consumer demand; the payment terminal may include, but is not limited to: smart phones (e.g., android phones, iOS phones, etc.), tablet computers, portable personal computers, mobile internet devices (Mobile Internet Devices, abbreviated as MID), biometric acquisition devices, etc. smart devices.

The backend service device 102 may be a backend server, which may include, but is not limited to: data processing servers, web servers, application servers, and the like, have complex computing capabilities. Specifically, the backend service device 102 may be a backend server of the payment terminal 101, or the backend service device 102 may be a backend server of an application running in the payment terminal 101 for interacting with the payment terminal 101 running the application to provide computing and application service support for the any application. The back-end service device 102 may also be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers; the backend service device 102 may also be a blockchain device in a blockchain network. The payment terminal 101 and the back-end service device 102 may be directly or indirectly connected in communication through a wired or wireless manner, and the connection manner between the payment terminal and the back-end service device is not limited in the embodiment of the present application.

The payment processing scheme proposed in the embodiment of the present application may be executed by the payment terminal 101, or jointly executed by the payment terminal 101 and the backend service device 102; in the following, in conjunction with fig. 1b, and taking an example that the payment terminal 101 and the back-end service device 102 execute a payment processing scheme together, the payment terminal and the back-end service device are further described, as shown in fig. 1 b:

The payment terminal 101 may be capable of running a biometric application, for example, when the biometric feature is a facial feature, the biometric application is a facial recognition application, and the facial recognition application may include: a face acquisition and recognition module, a face cutting and calculating module (or called an image cutting module), a payment confirmation module and a result page module (or called a payment result module), and the like. Wherein: face acquisition and recognition module: invoking a camera (such as the aforementioned 3D camera) of the payment terminal 101 to collect face data (such as collecting face images, face snap images or face streaming media data, i.e. video streams (including multi-frame face images)) of payment objects (such as the number of payment objects is N, N is an integer greater than 1) on the payment terminal side; and after the face streaming media data is obtained, the face streaming media data is supported to be optimized, and an initial image with highest comprehensive evaluation is selected. Face cutting calculation module: cutting face parts in the face simultaneous image with highest comprehensive evaluation identified by the face recognition module to obtain face images of each payment object, wherein each face image comprises face parts of different payment objects contained in the face simultaneous image. And (3) confirming a payment module: object information for displaying a target payment object selected from the N face images; when the confirmation payment module receives the target payment object to confirm execution of the target order, interaction can be performed with the back-end service device, so that the back-end service device deducts electronic resources required by the order object in the target order from an account of the target payment object, and adds the electronic resources to an account of a merchant to which the order object belongs. The result page module: status information for displaying a target order, the status information including: successful payment, failure of payment, ongoing payment, failure of payment, etc.

The backend service device 102 may provide backend services, which may include, but are not limited to: biometric services (such as face recognition services), payment policy services (or scoring election services), basic services, push services, etc. If the biometric identification service is a face identification service, the face identification service: the method is used for extracting the characteristics of the received face image, comparing the extracted characteristics with the characteristics in the characteristic database, finding out the characteristics with the highest matching degree, comparing the characteristics with the face in the face database according to the characteristics with the highest matching degree, and further identifying the identity of the payment object in the face image. Basic service: the payment system comprises a basic account service, a basic payment service and the like, wherein the basic account service is used for managing information related to a payment object, such as a password, a payment object name, identity information and the like of the payment object, and the basic payment service is used for managing object information related to the payment object, such as the payment by pulling a payment code of the payment object from the basic account service. Payment policy service: a service related to a face selection policy is provided, and the payment policy service is different according to the difference of the payment policies, for example, the payment policy is a visual recognition algorithm (or called a face value algorithm), and the payment policy service may score the face value as the selection service. Push service: the active pushing function for information, specifically, the long connection channel between the back-end service and the face recognition application program, is maintained, so that information can be actively pushed from the back-end service device 102 to the payment terminal 101.

It should be noted that the foregoing is merely a module or service that may be included in the payment terminal and the back-end service device, and is not limited to the embodiments of the present application. For example: when the payment terminal performs the election of the plurality of face parts, the payment terminal may include the scoring election service, and the backend service device may not include the scoring election service. And the following steps: the back-end service device according to the embodiment of the present application may include more than one back-end service device, for example, the back-end service device may include a payment processing device and a face recognition device, the face recognition device may be configured to perform operations such as recognition processing on a collected face image of a payment object, and the payment processing device may be configured to store object information of the payment object and perform operations such as payment processing on a target order. In addition, the payment processing scheme mentioned in the embodiment of the present application may be combined with a blockchain, so that the payment processing system shown in fig. 1a may be used as a data sharing system, and a back-end service device included in the payment processing system may be used as a node device in the blockchain network; thus, the back-end service device can upload the target order to the blockchain for storage, can store the biological characteristics (such as face characteristics) of each payment object to the blockchain, and the like. The security of biological characteristics and order storage can be improved, and the security and convenience of biological payment are ensured.

Based on the above-described payment processing scheme, a more detailed payment processing method is provided in the embodiments of the present application, and the payment processing method provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

FIG. 2 illustrates a flow diagram of a payment processing method provided in an exemplary embodiment of the present application; the payment processing method may be performed by a payment terminal, and may include, but is not limited to, steps S201 to S204:

s201: in response to a bio-payment request for a target order, a biometric of at least two payment objects is acquired.

When the payment object has a payment requirement for the target order, a bio-payment request for the target order for requesting payment processing of the target order using the bio-characteristics of at least two payment objects of the payment terminal side may be generated at the payment terminal side. For example, if three payment objects purchase an order object (such as any commodity) at a merchant, the merchant generates a target order related to any one of the three payment objects when information (such as data to be traded (such as purchase quantity) of the order object, unit price information, etc.) of the order object is input in the payment terminal according to the purchase condition of the three payment objects to the order object, and then determines to generate a bio-payment request for the target order when the collection of the biological characteristics of the three payment objects is started in the payment terminal. The above-mentioned biological payment request is a face payment request for requesting to pay for a target order by using face features of three payment objects in a face payment scenario; for convenience of explanation, the following description will take the biological payment request as the face payment request as an example, which is specifically described herein.

As described previously, the biometric of the payment object may include, but is not limited to: fingerprints, irises, faces, DNA, etc., the examples of this application are described with biological features as facial features. Specifically, a 3D camera is deployed in the payment terminal, when the payment object has a requirement for paying the target order, the merchant can start a face recognition application deployed in the payment terminal, and the face recognition application can call the 3D camera of the payment terminal to collect face images of at least two payment objects on the payment terminal side. Wherein any one of the face images contains biometric features (or facial features, face features) of one of the payment objects, which can be used to determine the identity of the one payment object; for example, the face image of the payment object X includes a face portion (or a face feature) of the payment object X, from which the identity of the payment object X can be determined.

Specifically, in response to a bio-payment request for a target order, embodiments of the present application provide two ways to collect the biometric of at least two payment objects; taking a biological payment request as a face payment request, taking the biological characteristics of a payment object as face characteristics as an example, and providing specific implementation processes of two face image acquisition modes; wherein:

1) Face images of each of at least two payment objects are acquired separately. In detail, assuming that the at least two payment objects include a first payment object and a second payment object, in response to a bio-payment request for a target order, a biometric feature (such as a face image) of the first payment object and a biometric feature (such as a face image) of the second payment object may be sequentially acquired within a set time range, so as to obtain biometric features of the at least two payment objects. The set time range when the face image is collected may be: in the one-time payment process, starting to start the 3D camera, and passing a time range of a target time period; for example, the 3D camera is started to acquire the face at 12:00, the target time period is 2 minutes, and then the set time range may include 12:00 to 12:02.

In connection with fig. 3a, taking at least two payment objects including a first payment object and a second payment object as an example, a procedure of calling a 3D camera of a payment terminal to sequentially collect face images of the first payment object and the second payment object is given. As shown in fig. 3a, in response to a face payment request for a target order, a 3D camera is started to perform face acquisition on a first payment object and a second payment object; assuming that the first payment object is located within the acquisition range of the 3D camera (such as the spatial range that the 3D camera can capture) at the first time within the set time range (such as within 2 minutes after the 3D camera is started), determining that the face image 301 of the first payment object can be acquired at the first time; assuming that at a second time (within a set time range and at any time later than the first time), the second payment object is within the acquisition range of the 3D camera, determining that a face image 302 of the second payment object is acquired at the second time; based on this, it can be determined that the face image 301 of the first payment object and the face image 302 of the second payment object are sequentially acquired within the set time range.

2) A biometric snap image is acquired that includes at least two payment objects. In detail, assuming that at least two payment objects include a first payment object and a second payment object, a biometric image (e.g., a face-in-time image) including the first payment object and the second payment object may be acquired, and a face image of the first payment object and a face image of the second payment object may be segmented from the biometric image. When the biometric image is a face image, the process of dividing the face image of the first payment object and the face image of the second payment object from the face image may be performed based on a face cutting calculation module included in the face recognition application program, and may specifically include: firstly, face recognition processing is carried out on the face simultaneous image, and the display position of the face part of each payment object in the face simultaneous image is recognized; then, according to the display position of the face part of each payment object, cutting the face snap image to obtain the face image of each payment object in the face snap image, for example, cutting to obtain the face image of the first payment object and the face image of the second payment object.

A process of calling a 3D camera of the payment terminal to acquire a face snap image containing at least two payment objects is given in combination with fig. 3 b; as shown in fig. 3b, it is assumed that at least two payment objects to be photographed include: the first payment object, the second payment object, the third payment object, the fourth payment object and the fifth payment object can be called to collect face snap images 303 of the first payment object, the second payment object, the third payment object, the fourth payment object and the fifth payment object by the 3D camera in response to the face payment request for the target order, namely, five payment objects are snapped at a time; and then cutting the face snap image 303 to obtain a face image 3031 of the first payment object, a face image 3032 of the second payment object, a face image 3033 of the third payment object, a face image 3034 of the fourth payment object and a face image 3035 of the fifth payment object.

It should be noted that, through both the above two implementation manners, the biological characteristics of at least two payment objects can be acquired, and the embodiment of the application is not limited to which specific acquisition manner is adopted. In addition, in the process of collecting the biometric characteristics of the at least two payment objects, the process of starting the biometric application program (such as the face recognition application program) in the payment terminal may be performed by any one of the at least two payment objects, for example, any one payment object triggers an operation (such as a gesture operation, a key operation, an audio operation, etc.) of starting the biometric application program on the payment terminal. Or may be executed by a merchant corresponding to the payment terminal, which is not limited in the embodiment of the present application, for a payment object that initiates the biometric application in the payment terminal. In addition, the types and styles of the payment terminals are not limited to those shown in fig. 3a or fig. 3b, and in other application scenarios, the payment terminals may also be smart terminals (such as smart phones and cash registers) held by merchants, which are not limited in the embodiments of the present application.

The 3D camera provided by the embodiment of the application not only collects single-frame face images of a payment object, but also supports collection of face streaming media data. The face streaming media data comprises a plurality of frames of face images, so that after the face acquisition and recognition module contained in the face recognition application program acquires the face streaming media data, the face streaming media data can be optimized through the optimizing sub-module in the face acquisition and recognition module to obtain an optimal face image, and the optimal face image is sent to the back-end service equipment for subsequent processing. The preferred implementation manner of the face acquisition and recognition module for the face streaming media data can comprise: the face acquisition and recognition module comprehensively evaluates a plurality of frames of face images contained in the face streaming media data according to evaluation indexes (such as face size, face angle and the like in the face images, contrast, brightness, definition and the like of the face images), takes the face image with the highest comprehensive evaluation as the face image to be sent to the back-end service equipment, and the face image with the highest comprehensive evaluation contains abundant and obvious face characteristics, so that the back-end service equipment is facilitated to execute subsequent operations based on the face image with the highest comprehensive evaluation, and further accuracy and rapidity of identifying payment objects are improved. In addition, in order to ensure that the shooting objects of the 3D camera are real payment objects in shooting scenes, but not people in photos, posters, paintings and other data, the face acquisition and recognition module provided by the embodiment of the application further comprises a biopsy sub-module, and the biopsy sub-module can filter people which are not really in the shooting environment in the process of recognizing the face part in the face in-time image, so that the face of other people is prevented from being stolen for payment processing, and the payment safety is improved.

S202: a target payment object is determined from the at least two payment objects.

Wherein when any one or more of the at least two payment objects is determined to be the target payment object, it is indicated that the target order is to be paid by the target payment object; in the payment scene, the mode of selecting one or more payment objects from the plurality of payment objects to pay the target order can enrich the payment form, and the interestingness of the payment processing process is improved. In particular implementations, embodiments of the present application provide various implementations for determining a target payment object from at least two payment objects, including, but not limited to: the target payment object is determined from the at least two payment objects by the payment object or from the at least two payment objects by a payment policy.

In one implementation, the target payment object is determined from at least two payment objects by a payment object (e.g., any one of the at least two payment objects). In detail, when the biological characteristics of at least two payment objects are acquired in response to a biological payment request for a target order, the embodiment of the application supports outputting a payment object selection list, wherein the payment object selection list contains the biological characteristics of each payment object in the acquired at least two payment objects; in response to a selection operation of the payment object selection list, the selected payment object may be determined as a target payment object. Wherein, according to the different display style of the payment object selection list, the selection operation of the payment object selection list is different. For example: taking biological characteristics as face images as an example, the face image of each payment object in the payment object selection list corresponds to one selection identifier, and when any one or more selection identifiers corresponding to the face images are selected, the payment object corresponding to the face image corresponding to the selected selection identifier is determined to be a target payment object. And the following steps: the payment object selection list comprises a confirmation option, and when the confirmation option is clicked, all payment objects contained in the payment object selection list are taken as target payment objects; etc.

An exemplary schematic diagram of determining a target payment object from at least two payment objects by the payment objects can be seen in fig. 4a, as shown in fig. 4a, after face images of each payment object in at least two payment objects are collected, a payment object selection list 401 is output, where the payment object selection list 401 includes a face image 4011 of a first payment object, a face image 4012 of a second payment object, a face image 4013 of a third payment object, a face image 4014 of a fourth payment object, and a face image 4015 of a fifth payment object, and a display area where each face image is located includes a selection identifier; when the selection identifier corresponding to the face image 4015 of the fifth payment object is selected, which indicates that the payment object wants to pay the target order by the fifth payment object, the fifth payment object is determined as the target payment object.

In other implementations, the target payment object is determined from at least two payment objects by a payment policy. In detail, the biological characteristics of each payment object in at least two payment objects can be scored according to the payment strategy, so as to obtain the score of each payment object; and selecting a target payment object from at least two payment objects based on the score of each payment object. The payment policies are different according to different types of the biological characteristics, and take the biological characteristics as a face image as an example, and the payment policies include but are not limited to: visual recognition algorithms, behavior prediction rules or appearance similarity rules; the visual recognition algorithm may be referred to simply as a face value prediction algorithm (facial beauty prediction, FBP), which is a visual recognition problem that uses a face value factor (e.g., a random factor) to evaluate the face image for the face charm. The behavior prediction rule is a rule for evaluating the behavior of a payment object through behavior data of the payment object; the behavior data may refer to data such as payment times, payment electronic resource amount, payment field and the like within a preset time period before the current time. The appearance similarity rule is a rule for evaluating the similarity between the face features of the payment object and the face features of the reference object; for example, the degree of similarity between the face features of the payment object and the appearance of a certain star may be evaluated by the appearance similarity rule. The embodiment of the present application will be described herein only briefly, and a specific implementation process of determining a target payment object from at least two payment objects according to a payment policy may be referred to the subsequent embodiment, which will not be described herein in detail.

It is noted that, before determining the target payment object from the at least two payment objects based on the payment policy, the embodiment of the present application supports outputting the payment object selection list first, so that the payment object can confirm the face image of the payment policy to be performed, and only after the payment object confirms the face image in the payment object selection list, the step of determining the target payment object from the at least two payment objects based on the payment policy is triggered to be performed. As shown in fig. 4b, a confirmation option 402 is included in the payment object selection list, and when the confirmation option 402 is triggered, all face images in the face images representing that the payment object wants at least two payment objects participate in subsequent scoring processing, i.e. the target payment object is elected from all face images in the payment object selection list based on the payment policy. Or if the confirmation operation of at least one payment object in the at least two payment objects is detected in the payment object selection list, scoring the face image of each payment object in the at least one payment object according to a payment processing strategy to obtain the score of each payment object; that is, the embodiment of the application also supports the participation of the face image of the selected part of the payment object in the payment object selection list in the scoring process. Wherein the validation operation for at least one of the at least two payment objects may comprise: single click, double click, drag and other operations performed on the face image of at least one of the at least two payment objects; the embodiment of the present application does not limit the specific implementation of the confirmation operation of determining at least one payment object to which the payment policy is executed from among at least two payment objects, and is described herein.

Of course, if the payment object is not satisfied with the biological characteristics (such as face images) of at least two payment objects contained in the payment object selection list, for example, face images of one or more payment objects are omitted in the payment object selection list, and further, the form represented by the face images contained in the payment object selection list is not attractive, etc., the embodiment of the present application further supports the re-acquisition of the biological characteristics of at least two payment objects. Specifically, taking a biometric feature as an example of a face image, referring to fig. 4b, a re-shooting option 403 (or a button, a control, and a component) is included in the payment object selection list, when the re-shooting option 403 is selected, which indicates that the payment object wants to re-shoot face images of at least two payment objects, the 3D camera is restarted for shooting. In addition, the embodiment of the present application is not limited to the display style of the payment object selection list, and the payment object selection list may be displayed by several display styles shown in fig. 4c in addition to the display style shown in fig. 4a and 4b, for example, the collected face snap images may be displayed together at the payment object selection list, for example, the arrangement display mode of each face image in the payment object selection list may be displayed corresponding to the station positions of each payment object in the face snap images, and so on.

S203: object information of the target payment object is obtained according to the biological characteristics of the target payment object.

Wherein, the object information of the target payment object may refer to information generated when the target payment object is registered as a new user of the target application, and the object information may include: information such as a payment object avatar, a payment object account number, a payment object nickname, a biometric feature (e.g., a facial feature, a fingerprint feature, etc.), etc. The target application program may refer to an application program with a payment function (such as a social application program with a payment function) running in an intelligent terminal (such as a smart phone, a personal computer, etc.) used by the target payment object; the target payment object may log into the target application using an account number registered in the target application so that the target payment object can use services or functions provided by the target application. The target applications may include, but are not limited to: (1) IM (Instant Messaging ) applications, which refer to internet-based applications that communicate messages and social interactions in an instant, such as map applications, gaming applications, etc. that include social interaction functionality. (2) The content interaction application program refers to an application program capable of realizing content interaction, and can be, for example, an application program such as an internet bank, a personal space and the like; etc. Depending on the manner in which the application is run, the target application may include, but is not limited to: (1) a client, a so-called client (also referred to as an application client, APP client), refers to an application installed and running in a terminal. (2) The target application may also refer to an installation-free application, i.e. an application that can be used without downloading an installation, which is also commonly known as an applet, which is typically run as a sub-program in the client. (3) The target application may also refer to a web application that is opened through a browser; etc. The embodiment of the application does not limit the type of the target application.

The above-mentioned registration may refer to a process that after the target payment object submits a registration request to the target application program, the registration request carries registration information (such as a payment object name and a password) of the target payment object, and a server (such as a back-end service device) of the target application program performs a registration operation; wherein the registering operation includes: checking whether the user name conflicts, whether the password meets the requirements, checking whether key information (such as a mobile phone number, real name authentication information and the like) is real, and the like, after the user name is registered successfully, storing the registration information of the target payment object, and distributing an identity mark capable of uniquely determining the identity of the payment object to the target payment object, wherein the target payment object is a registered user of the target application program. Wherein, as described in the foregoing, the target application refers to an application with a payment function running in the intelligent terminal used by the target payment object; if the target payment object is registered as a new user in the target application program or the biological characteristics of the target payment object are added when the information of the target payment object is perfected, binding the biological characteristics of the target payment object with the object information of the target payment object; thus, the object information of the target payment object can be retrieved through the biological characteristics of the target payment object; for example, if the biometric feature of the target payment object is a face feature, the object information of the target payment object may be retrieved through the face image of the target payment object.

S204: and carrying out payment processing on the target order based on the object information of the target payment object.

Wherein the target payment object may include an account of the target payment object, then performing payment processing on the target order based on the target payment object may include: and deducting the electronic resources required by the order object in the target order from the account of the target payment object, and adding the electronic resources required by the order object in the account of the merchant to which the order object in the target order belongs, so as to realize the payment processing of the target order. Of course, according to different practical application scenarios, the payment processing procedure for the target order based on the object information of the target payment object is not limited to the above procedure; for example: when the balance in the account of the target payment object is smaller than the electronic resources required by the order object, and the account of the target payment object is bound with an entrance of a third party payment (such as bank card payment, credit card payment or debit and the like), the electronic resources required by the order object can be deducted from the third party payment through the account of the target payment object.

In addition, before performing payment processing on the target order based on the object information of the target payment object, the embodiments of the present application further support outputting the object information of the target payment object for confirmation by the payment object (e.g., any one or more of the at least two payment objects); when the object information of the target payment object is confirmed, the step of executing the payment processing of the target order based on the object information of the target payment object is triggered only when the payment object wants to pay the target order based on the target payment object. In a specific implementation, when object information of a target payment object is acquired, outputting a payment object confirmation interface, wherein the payment object confirmation interface comprises the object information of the target payment object; triggering and executing the step of carrying out payment processing on the target order based on the object information of the target payment object in response to a payment confirmation event of the payment object confirmation interface; wherein the payment confirmation event may include: an event generated when a confirmation operation is performed on the object information of the target payment object, for example, a confirmation option is included in the payment object confirmation interface, and when the confirmation option is triggered, it is determined that there is a confirmation operation on the object information of the target payment object; or, if the display time of the object information of the target payment object in the payment object confirmation interface is longer than the time threshold, for example, the time threshold is 30 seconds, starting from the display of the object information of the target payment object in the payment object confirmation interface, if there is no operation of canceling the payment using the object information of the target payment object in the payment object confirmation interface within 30 seconds, determining that the payment confirmation event is generated.

A schematic view of a payment object validation interface can be seen in fig. 5, as shown in fig. 5, including object information 5011 of a target payment object in the payment object validation interface 501, the object information 5011 including, but not limited to: the header of the target payment object, the nickname, the identification of the login device (e.g., the device's serial number SN, the ID that can uniquely identify the device), etc. Also included in the payment object validation interface are a validation option 502 and a re-beat option 503; when the confirmation option 503 is selected, determining that there is a payment confirmation event in the payment object confirmation interface, which means that the payment object confirmation performs payment processing on the target order using the object information of the target payment object displayed in the payment object confirmation interface 501; when the re-shooting option 503 is triggered, the payment object does not want to use the object information of the target payment object displayed in the payment object confirmation interface 501 to perform payment processing on the target order, and then the 3D camera of the payment terminal is restarted to collect face images of at least two payment objects, and the implementation manner is re-executed. According to the method, before the target order is paid based on the object information of the target payment object, the object information of the target payment object is output for the payment object to confirm, so that the target payment object of the target order of the payment object can be informed in time, and the payment object can know the payment condition of the target order.

It may be appreciated that, after performing payment processing on the target order based on the object information of the target payment object, the embodiment of the present application further supports outputting a payment processing result, which may specifically be output on a display screen of the payment terminal, and/or output on a terminal screen of an intelligent terminal used by the target payment object. Wherein the payment processing result may indicate success or failure of payment for the target order based on the object information of the target payment object; when the electronic resources required by the order object are successfully transferred from the account of the target payment object to the account of the merchant to which the order object belongs, determining that the payment processing result is successful in payment processing; otherwise, when the electronic resource required by the order object is not successfully transferred from the account of the target payment object to the account of the merchant to which the order object belongs, determining that the payment processing result is failure of the payment processing. An exemplary schematic diagram of outputting the payment processing result in the smart terminals used by the payment terminal and the target payment object may be referred to as fig. 6, and when the back-end service device performs payment processing on the target order based on the object information of the target payment object to obtain the payment processing result, the back-end service device may transmit the payment processing result to the smart terminals used by the payment terminal and the target payment object so that the smart terminals used by the payment terminal and the target payment object display the payment processing result, as shown in fig. 6. Optionally, a long connection channel is established between the back-end service device and the payment terminal (or the face recognition application program running in the payment terminal) and the intelligent terminal used by the target payment object (or the target application program running in the intelligent terminal), so that the back-end service device can automatically push the payment processing result to the payment terminal and the intelligent terminal used by the target payment object for display after obtaining the payment processing result.

In the implementation of the application, in response to a biological payment request for a target order, biological characteristics of at least two payment objects can be acquired; and supporting determination of a target payment object from the at least two payment objects such that payment processing of the target order may be performed using object information of the target payment object. By the scheme, biological images of at least two payment objects can be acquired in the payment processing process, and the target payment objects are determined based on the biological characteristics of the at least two payment objects to pay the target order; compared with the payment based on the target order by a single user, the method and the device can realize that at least two users in a payment scene pay the target order, enrich the biological payment form and further facilitate the popularization and propagation of the biological payment.

FIG. 7 is a flow chart of a payment processing method according to an exemplary embodiment of the present application; the payment processing method may be commonly performed by the above-mentioned payment terminal and back-end service device, and may include, but is not limited to, steps S701-S709:

s701: the payment terminal collects biometric characteristics of at least two payment objects in response to a biometric payment request for a target order.

It should be noted that, the specific implementation process shown in step S701 may refer to the description of the specific implementation process shown in step S201 in the embodiment shown in fig. 2, which is not repeated herein.

S702: the payment terminal outputs the biological characteristics of at least two payment objects, and if the confirmation operation of the biological characteristics is detected, a payment request is generated.

If the payment terminal sequentially collects the biological characteristics of at least two payment objects within the set time range, determining that the biological characteristics of at least two payment objects are collected when the biological characteristics of the last payment object in the at least two payment objects are collected. If the target payment object is a biological feature snap image of at least two payment objects collected at one time, the payment terminal also performs cutting processing on the biological feature snap image through a cutting calculation module in the biological recognition application program (for example, a face cutting calculation module in the face recognition application program is adopted to cut the face snap image), so as to obtain the biological feature of each payment object in the at least two payment objects. Regardless of which implementation is described above, the biometric of the at least two payment objects is obtained, and the biometric of the at least two payment objects is output for verification by the payment object (e.g., one or more of the at least two payment objects). The term "biometric confirmation" is to be understood in a simple manner as: the identified biometric is a biometric that requires continued participation in a subsequent operation (e.g., scoring process), and the unconfirmed biometric is a biometric that does not require continued participation in a subsequent operation. Based on this, the confirmation of the biometric feature may include: the biometric of each of the at least two payment objects is confirmed, or the biometric of at least one of the at least two payment objects is confirmed only. Wherein:

(1) If a confirmation operation of the biological characteristics of each of the at least two payment objects is detected, which indicates that the payment object wants all the biological characteristics of the at least two payment objects to participate in subsequent scoring processing, a payment request is generated based on the biological characteristics of the at least two payment objects and the target order, wherein the payment request is used for requesting to determine the target payment object from the biological characteristics of the at least two payment objects. Wherein the validation operation of the biometric for each of the at least two payment objects may include, but is not limited to: a triggering operation (e.g., a single click, a double click, a long press, etc.) performed on the confirmation option, an operation generated when a display time of a biometric feature (e.g., a face image) of at least two payment objects is longer than a preset time period, or a selection operation of a biometric feature of each of the at least two payment objects, etc. An exemplary operation for confirming the face image of each of the at least two payment objects may refer to a description of the implementation procedure shown in fig. 4b in the embodiment shown in fig. 2, which is not repeated herein.

(2) If a confirmation operation for at least one of the at least two payment objects is detected, indicating that the payment object only wants the biometric feature (e.g., a face image) of the at least one of the at least two payment objects to participate in the scoring process, a payment request is generated based on the confirmed biometric feature of the at least one payment object and the target order, where the payment request is used to request a determination of the target payment object from the confirmed biometric feature of the at least one payment object. An exemplary interface schematic of selecting at least one face image of at least two payment objects to participate in the subsequent scoring process can be seen in fig. 8, where, as shown in fig. 8, a close option is displayed in an area where the face image of each payment object is located, for example, a close option 8011 is displayed in a display area where the face image 801 of the first payment object is located, a close option 8021 is displayed in a display area where the face image 802 of the second payment object is located, and so on; when the closing option in the display area where the face image of any payment object is located is triggered, the face image corresponding to the triggered closing option does not participate in subsequent scoring processing, if the closing option 8011 in the display area where the face image 801 of the first payment object is located is triggered, it is determined that scoring processing is only performed on face images of other payment objects except the first payment object in the at least two payment objects, that is, a payment request is generated based on face images of other payment objects except the first payment object and a target order in the at least two payment objects. It should be understood that fig. 8 is merely provided as an exemplary implementation of selecting a face image, and that other implementations of adaptively selecting a face image are equally applicable to embodiments of the present application.

S703: and the payment terminal sends the payment request to the back-end service equipment.

S704: the backend service device determines a target payment object from the at least two payment objects.

In steps S703-S704, after the back-end service device receives the payment request sent by the payment terminal, the back-end service device may determine a target payment object from at least two payment objects according to the payment policy in response to the payment request. As described in the foregoing embodiments, the payment policy may include a plurality of types, and the implementation of determining the target payment object from at least two payment objects according to the payment policy is not the same according to the payment policy. The following takes biological characteristics as face images, and the payment strategy comprises: the visual recognition algorithm, the behavior prediction rule and the appearance similarity rule, and the at least one payment object selected from the at least two payment objects includes, for example, all payment objects in the at least two payment objects (i.e., the at least one payment object is at least two payment objects), and the implementation process of scoring face images in the at least two payment objects according to a payment policy to obtain the score of each payment object is described, where:

In one implementation, the payment policy includes a visual recognition algorithm. The visual recognition algorithm can comprise a face value algorithm, wherein the face value algorithm adopts the idea of average face to score the face image; specifically, a face image is used as input information of a face value algorithm, so that the face value algorithm firstly detects feature points in the face image, then the face image is divided into different areas, then the different areas are subjected to sectional radiation transformation, and then the results of the sectional radiation transformation of the different areas are subjected to weighted average to obtain the face value scoring score of the face image. The face value algorithm can comprehensively consider the shape characteristics and texture characteristics of the face, such as symmetrical facial features, symmetrical outlines and attractive complexion faces in the face image, which are more likely to be favored by the masses, so that the face value scoring score of the face image is higher.

An exemplary schematic diagram of scoring face images of at least two payment objects according to a visual recognition algorithm can be seen in fig. 9a, where as shown in fig. 9a, a face image 901 of a first payment object, a face image 902 of a second payment object, a face image 903 of a third payment object, a face image 904 of a fourth payment object, and a face image 905 of a fifth payment object are obtained by cutting from face in-time images; the visual recognition algorithm is assumed to be adopted to score the face image of the face of each payment object in at least one payment object (namely at least two payment objects), so that the visual recognition score of each payment object is respectively: the visual face value score of the first payment object is 40 points, the visual face value score of the second payment object is 80 points, the visual face value score of the third payment object is 70 points, the visual face value score of the fourth payment object is 60 points, and the visual face value score of the fifth payment object is 50 points; then a target payment object may be selected from the at least one payment object based on the score of each payment object. Rules for selecting a target payment object according to a score may include, but are not limited to: selecting one or more payment objects as target payment objects according to the order of the scores from high to low; alternatively, one or more payment objects with scores greater than a score threshold are targeted payment objects. For example, the sequence obtained by sorting five payment objects in order of the scores from high to low is: second payment object- & gt third payment object- & gt fourth payment object- & gt fifth payment object- & gt first payment object; the two payment objects with higher scores are a second payment object and a third payment object, and the rule for selecting the target payment object according to the scores comprises: and selecting the two payment objects as target payment objects according to the order of the scores from high to low, and determining the second payment object and the third payment object as target payment objects. The above description is given by taking the number of the target payment objects as two as an example, and of course, the number of the target payment objects may be only one, for example, the payment object with the highest score is taken as the target payment object.

In another implementation, the payment policy includes behavior prediction rules. The behavior prediction rule is used for selecting a target payment object from at least two payment objects according to historical behavior data of the payment objects; specifically, first, face recognition processing is performed on a face image of each payment object in at least one payment object (i.e., at least two payment objects), for example, face recognition processing is performed on the face image based on a face recognition module provided by a back-end service, so as to obtain an identifier of each payment object, an identifier of any payment object may be used to uniquely mark an identity of the any payment object, for example, the identifier of any payment object may include a payment object account number and the like; then, according to the identification of each payment object, behavior data corresponding to each payment object is obtained, where the behavior data may include, but is not limited to: number of payments, amount of electronic resources paid, field of payment, etc.; finally, scoring the behavior data corresponding to each payment object according to the behavior prediction rule to obtain the behavior prediction score of each payment object; and selecting a target payment object from at least one payment object based on the behavior prediction score of each payment object.

An exemplary schematic diagram of scoring face images of at least two payment objects according to a behavior prediction rule can be seen in fig. 9b, where as shown in fig. 9b, a face image 901 of a first payment object, a face image 902 of a second payment object, a face image 903 of a third payment object, a face image 904 of a fourth payment object, and a face image 905 of a fifth payment object are obtained by cutting from face in-time images; assuming that after face recognition is performed on face images of each payment object in the face snap image, payment times of each obtained payment object in a historical time period (for example, a target time before a time of obtaining behavior data is long) are respectively as follows: the first payment object was paid 3 times, the second payment object was paid 5 times, the third payment object was paid 1 time, the fourth payment object was paid 8 times, and the fifth payment object was paid 4 times. The rules for selecting the target payment object according to the score include: taking the payment object with the highest number in the behavior data of the payment times as a target payment object, scoring the payment object under the implementation mode to obtain the number of times of the payment times, scoring the behavior data corresponding to each payment object according to a behavior prediction rule, and sequencing the five payment objects according to the order of the scores from high to low to obtain a sequence of: fourth payment object → second payment object → fifth payment object → first payment object → third payment object; the payment object with the highest score is the fourth payment object, i.e. the fourth payment object is determined as the target payment object.

It should be noted that the above description is given by taking the behavior data as an example of "payment times" only; in other implementations, behavioral data may also be scored for at least two data types. For example: the behavior data are "payment times" and "payment fields", and in this implementation, the weight scores of the two behavior data of each payment object may be calculated according to the weights, and the target payment object may be determined according to the weight scores of each payment object. When the "payment number" of the first payment object is 3 and the number of "payment fields" of the first payment object is 4, the weight scores of the two behavior data of the first payment object are calculated to be 3×40% +4×60% =3.6. The type and amount of behavior data are not limited in this embodiment, and are described herein.

In other implementations, the payment policy includes appearance similarity rules. The appearance similarity rule is to select a target payment object from at least two payment objects according to the similarity between the appearance of the payment object and the appearance of the reference object; in particular, a reference biometric of a reference object is obtained, which may be any payment object; then, according to the appearance similarity rule, calculating the appearance similarity between the face image of each payment object in at least one payment object and the reference biological feature; obtaining a sample similarity score of each payment object based on the sample similarity between the face image of each payment object and the reference biological feature, for example, directly taking the sample similarity as the sample similarity score; finally, selecting a target payment object based on the appearance similarity score of each payment object.

An exemplary schematic diagram of scoring face images of at least two payment objects according to a feature similarity rule can be seen in fig. 9c, where as shown in fig. 9c, a face image 901 of a first payment object, a face image 902 of a second payment object, a face image 903 of a third payment object, a face image 904 of a fourth payment object, and a face image 905 of a fifth payment object are obtained by cutting from face in-time images; assuming that the obtained similarity scores of the appearance of each payment object are respectively as follows after the similarity comparison of the appearance of the face image of each payment object and the reference biological feature: the similarity of the appearance of the first payment object is 20%, the similarity of the appearance of the second payment object is 70%, the similarity of the appearance of the third payment object is 65%, the similarity of the appearance of the fourth payment object is 30% and the similarity of the appearance of the fifth payment object is 32%. The rules for selecting the target payment object according to the score include: taking the payment object with the highest score in the appearance similarity scores as a target payment object, scoring the payment object under the implementation mode to obtain a score of 'appearance similarity', scoring the behavior data corresponding to each payment object according to a behavior prediction rule, and sequencing the five payment objects according to the order of the scores from high to low to obtain a sequence which is as follows: second payment object- & gt third payment object- & gt fifth payment object- & gt fourth payment object- & gt first payment object; the payment object with the highest score is the second payment object, i.e. the second payment object is determined as the target payment object.

In summary, the payment policy is added in the payment scene to select the target payment object from multiple people to pay the target order, so that the interestingness of the payment scene can be improved, and the face payment can be promoted better. In addition, the foregoing is merely provided to illustrate three exemplary implementation processes for selecting the target payment object according to the payment policy, and is not limited to the embodiments of the present application.

In order to improve entertainment and interestingness of a payment scene, the embodiment of the application also supports the selection of a payment strategy by a payment object; when the payment object selects the payment strategy, scoring the face image of each payment object in at least two payment objects by adopting the payment strategy selected by the payment object; when the payment object does not select the payment policy, the background service device may randomly select the payment policy, or score the face image of each of the at least two payment objects using a default payment policy. The implementation manner of selecting the payment policy by the payment object may include: outputting policy identifications of one or more payment policies, such as outputting the policy identifications of the one or more payment policies together in an interface displaying face images of each of the at least two payment objects, or outputting the policy identifications of the one or more payment policies in a separate interface; in response to a selection operation of a target policy identifier (e.g., a policy identifier of any of the payment policies), determining that the payment policy is the payment policy corresponding to the selected target policy identifier.

An exemplary interface schematic of outputting a policy identification of a payment policy can be seen in FIG. 10; as shown in fig. 10, one or more payment policies, such as payment policy 1, payment policy 2, payment policies 3, … …, are output in an interface where the payment object selection list is located; when any one of the payment policies is selected, it is indicated that the payment object wants to score the biometric of at least two payment objects using the selected payment policy. Of course, the payment object can also select a plurality of payment strategies, and in this implementation manner, the biological characteristics of at least two users can be respectively scored based on the selected plurality of payment strategies, so as to obtain the score of each payment object under the plurality of payment strategies; one or more target payment objects corresponding to each payment strategy can be determined, so that a final target payment object can be determined from a plurality of target payment objects corresponding to a plurality of payment strategies to carry out payment processing on a target order; the playing method of determining the target payment object in the payment scene is further enriched, and the interestingness and entertainment of the payment process are improved.

S705: object information of the target payment object is acquired based on the biometric feature of the target payment object.

Taking biological characteristics as a face image as an example, the process of obtaining object information of a target payment object by the back-end service device based on the face image of the target payment object may include: firstly, carrying out feature extraction processing on a face image of a target payment object to obtain N face features of the face image of the target payment object, wherein N is an integer greater than 1; and secondly, comparing the N face features with candidate face features contained in the face feature database to obtain the matching degree of each face feature in the N face features, wherein the higher the matching degree is, the higher the similarity between the face features of the face image and the candidate face features is. Finally, determining the face feature with the highest matching degree from the matching degrees of the N face features; then matching the target face image associated with the face feature corresponding to the highest matching degree in a face database according to the face feature corresponding to the highest matching degree; and taking the object information associated with the target face image as the object information of the target payment object. The above-mentioned face feature database and face database may refer to two databases for storing different types of data, where the databases may be a lightweight database (SQLite), which is a relational database management system that complies with ACID (that is, four basic elements (atomicity, or called insertibility), consistency, isolation, and durability) for correctly executing a database transaction.

S706: and the back-end service equipment sends the object information of the target payment object to the payment terminal.

S707: the payment terminal outputs a payment object confirmation interface and generates a payment confirmation request in response to a payment confirmation event to the payment object confirmation interface.

Embodiments of the present application also support modification of a payment object in a payment object validation interface to a target payment object, where modifying the target payment object may include: determining other payment objects (such as payment objects except for the payment object corresponding to the target payment object in the payment object confirmation interface in at least two payment objects) as target payment objects to replace the target payment object displayed in the payment object confirmation interface, namely adopting the new target payment object to carry out payment processing on the target order; or, using other payment objects and the target payment object in the payment object confirmation interface as target payment objects, namely using the new target payment object and the original target payment object in the payment object confirmation interface to jointly carry out payment processing on the target order; this increases the management rights of the payment object to the target payment object of the target order. In a specific implementation, a score list is displayed in a payment object confirmation interface, wherein the score list comprises the score of each payment object; when the target score in the score list is selected, taking the payment object corresponding to the target score as a target payment object, and displaying object information of the payment object corresponding to the target score in a payment object confirmation interface.

According to the difference of the selected operations of the target scores in the score list, the payment object corresponding to the selected target score can be used as a new target payment object to replace and display the target payment object which is displayed in advance in the payment object confirmation interface, or the payment object corresponding to the selected target score is used as a new target payment object to be displayed in parallel with the original target payment object in the payment object confirmation interface. Optionally, as a new target payment object, a schematic diagram of replacing a target payment object displayed in advance in the payment object confirmation interface with a payment object corresponding to the selected target score may be seen in fig. 11a, where the payment object confirmation interface 1101 includes object information of the target payment object 1, and further includes a score list 1102, where the score list 1102 includes a score (such as a visual recognition score, a behavior prediction score, or a appearance similarity score) of each of at least two payment objects; when a target score (e.g., any score) in the score list 1102 is triggered (or clicked, double clicked, long pressed, etc.), it is determined that the payment object corresponding to the target score is used as a new target payment object to replace the target payment object that is displayed in the payment object confirmation interface in advance. Optionally, the payment object corresponding to the selected target score is used as a new target payment object, and is displayed in parallel with the original target payment object in the payment object confirmation interface, referring to fig. 11b, the payment object confirmation interface 1101 includes object information of the target payment object 1, and further includes a score list 1102, where the score list 1102 includes a score (such as a visual recognition score, a behavior prediction score, or a appearance similarity score) of each of at least two payment objects; when the target score in the score list 1102 is dragged to the target direction, determining that the payment object corresponding to the target score is used as a new target payment object, and displaying the new target payment object in parallel with the original target payment object in the payment object confirmation interface; wherein the target direction may include: along any direction of the payment object confirmation interface, along the direction of the location of the target payment object in the payment object confirmation interface, and the like.

S708: and the payment terminal sends a payment confirmation request to the back-end service equipment.

Based on the difference in the target payment object in the payment object confirmation interface in step S707, the payment confirmation request herein is not the same; for example, if the target payment object (such as any payment object) is newly added as the target payment object in step S707, the payment confirmation request is used to request the payment processing of the target order by using the object information of the target payment object and the object information of the original target payment object in the payment object confirmation interface; for another example, if the target payment object is not newly added in step S707, the payment confirmation request is used to request the payment processing of the target order by using the object information of the original target payment object in the payment object confirmation interface; etc.

S709: the back-end service device performs payment processing on the target order based on the object information of the target payment object.

In a specific implementation, the back-end service device may obtain a payment code of the first payment object based on the biological feature of the target payment object, and invoke the basic payment service to implement payment processing on the target order based on the payment code of the target payment object. Wherein invoking the base payment service to effect payment processing of the target order based on the payment code of the target payment object may comprise: the account of the target payment object is obtained, the electronic resource required by the order object is deducted from the account of the target payment object according to the payment code of the target payment object, and the electronic resource is transferred to the account of the merchant to which the order object belongs, so that the payment processing of the target order based on the biological characteristics of the target payment object is realized. When the electronic resources required by the order object are successfully transferred from the account of the target payment object to the account of the merchant to which the order object belongs, determining that the payment processing result is that the biological payment processing is successful; otherwise, when the electronic resource required by the order object is not successfully transferred from the account of the target payment object to the account of the merchant to which the order object belongs, determining that the payment processing result is failure of the payment processing.

As described above, the number of target payment objects may be more than 1, and when the number of target payment objects is at least two, the embodiment of the present application supports deducting a part of the electronic resources required by the order objects from the account numbers of the plurality of target payment objects according to the weight values, respectively. Optionally, a deduction weight value may be determined according to the score of the target payment object; specifically, assuming that the number of target payment objects is 5, the scores of the respective target payment objects are respectively: the score of the first target payment object is 1 score, the score of the second target payment object is 2 scores, the score of the third target payment object is 4 scores, the score of the fourth target payment object is 2 scores and the score of the fifth target payment object is 1 score, the weight value of the first target payment object is 10%, the weight value of the second target payment object is 20%, the weight value of the third target payment object is 40%, the weight value of the fourth target payment object is 20% and the weight value of the fifth target payment object is 10% are determined; when the electronic resource required by the order object is 100 yuan, it is known that 10 yuan should be deducted from the account of the first target payment object, 20 yuan should be deducted from the account of the second target payment object, 40 yuan should be deducted from the account of the third target payment object, 20 yuan should be deducted from the account of the fourth target payment object and 10 yuan should be deducted from the account of the fifth target payment object according to the weight value.

Of course, the above is merely provided as an exemplary implementation process of deducting the electronic resources required by the order object from the accounts of the multiple target payment objects according to the weights occupied by the scores; embodiments of the present application are not limited to the specific implementation of electronic resources required to deduct orders from multiple target payment objects, as described herein.

S710: and the back-end service equipment returns the payment processing result to the payment terminal.

It should be noted that, the specific implementation process shown in step S710 may be referred to the description of the specific implementation process shown in step S204 in the embodiment shown in fig. 2, which is not repeated herein.

In the embodiment of the application, in response to a biological payment request for a target order, biological characteristics of at least two payment objects can be acquired, the target payment object is determined from the at least two payment objects based on a payment policy, and the target order is paid by adopting object information of the target payment object. Such determining a target payment object based on the biological characteristics of the plurality of users to make a payment for the target order; compared with the payment of the target order based on the biological characteristics of a single user, the method realizes a multi-person payment mode in the payment field, enriches the payment mode of biological payment, and is further beneficial to popularization and propagation of biological payment; and the method for determining the target payment object by adding the payment strategy in the payment scene improves the interestingness and entertainment of the payment scene to a certain extent and improves the payment experience of the payment object.

The foregoing details of the method of embodiments of the present application are set forth in order to provide a better understanding of the foregoing aspects of embodiments of the present application, and accordingly, the following provides a device of embodiments of the present application.

Referring to fig. 12, fig. 12 is a schematic structural diagram of a payment processing apparatus provided in an exemplary embodiment of the present application, where the payment processing apparatus may be installed on a payment terminal in the foregoing method embodiment, and the payment processing apparatus may be a payment terminal or a plug-in a face recognition application running on the payment terminal; the payment processing arrangement shown in fig. 12 may be used to perform some or all of the functions described above in connection with the method embodiments described in fig. 2 and 7. Wherein, the detailed description of each unit is as follows:

an acquisition unit 1201 for acquiring biological characteristics of at least two payment objects in response to a biological payment request for a target order;

a processing unit 1202 for determining a target payment object from at least two payment objects;

the processing unit 1202 is further configured to obtain object information of the target payment object according to the biological feature of the target payment object;

the processing unit 1202 is further configured to perform payment processing on the target order based on the object information of the target payment object.

In one implementation, the at least two payment objects include a first payment object and a second payment object, and the collecting unit 1201 is configured to, when collecting the biometric features of the at least two payment objects, specifically:

In one implementation, the processing unit 1202 is configured to, when determining the target payment object from at least two payment objects, specifically:

outputting a biometric of each of the at least two payment objects;

In one implementation, the payment policy includes a visual recognition algorithm, and the processing unit 1202 is configured to score, according to the payment policy, a biometric of each payment object in the at least one payment object, and obtain a score of each payment object, where the score is specifically configured to:

In one implementation, the payment policy includes a behavior prediction rule, and the processing unit 1202 is configured to score, according to the payment policy, a biometric of each payment object in the at least one payment object, and obtain a score of each payment object, where the score is specifically configured to:

In one implementation, the payment policy includes a look-alike rule, and the processing unit 1202 is configured to score, according to the payment policy, a biometric of each payment object in the at least one payment object, and obtain a score of each payment object, where the score is specifically configured to:

acquiring a reference biological characteristic of a reference object;

In one implementation, the processing unit 1202 is configured to, when selecting a target payment object from at least one payment object based on the score of each payment object, specifically:

In one implementation, the processing unit 1202 is further configured to:

According to one embodiment of the present application, each unit in the payment processing apparatus shown in fig. 12 may be separately or completely combined into one or several additional units, or some unit(s) thereof may be further split into a plurality of units with smaller functions, which may achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the payment processing apparatus may also include other units, and in practical applications, these functions may also be implemented with assistance from other units, and may be implemented by cooperation of a plurality of units. According to another embodiment of the present application, a payment processing apparatus as shown in fig. 12 may be constructed by running a computer program (including program code) capable of executing the steps involved in the respective methods as shown in fig. 2 and 7 on a general-purpose computing device such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and the payment processing method of the present application may be implemented. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and run in the above-described computing device through the computer-readable recording medium.

In this embodiment of the present application, in response to a face payment request for a target order, the acquisition unit 1201 may acquire face images of at least two users; the processing unit 1202 supports determining a payment object from at least two users such that payment processing may be performed on a target order using account information of the payment object. In the scheme, the acquisition of the face images of multiple people in the payment process is supported, and the payment object is determined based on the face images of the multiple people to pay the target order; compared with the current single face-brushing payment, the method can realize that at least two users in a payment scene pay the target order, enriches the face-brushing payment form, and is further favorable for popularization and propagation of face-brushing payment.

Fig. 13 is a schematic diagram illustrating a structure of a payment processing apparatus according to an exemplary embodiment of the present application. Referring to fig. 13, the payment processing apparatus includes a processor 1301, a communication interface 1302, and a computer readable storage medium 1303. Wherein the processor 1301, the communication interface 1302, and the computer readable storage medium 1303 may be connected by a bus or other means. Wherein the communication interface 1302 is for receiving and transmitting data. The computer readable storage medium 1303 may be stored in a memory of the payment processing apparatus, the computer readable storage medium 1303 storing a computer program including program instructions, the processor 1301 being configured to execute the program instructions stored in the computer readable storage medium 1303. Processor 1301, or CPU (Central Processing Unit ), is a computing core and a control core of the payment processing device, which is adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement a corresponding method flow or a corresponding function.

The embodiment of the application also provides a computer readable storage medium (Memory), which is a Memory device in the payment processing device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the payment processing device and an extended storage medium supported by the payment processing device. The computer readable storage medium provides a storage space that stores a processing system of the payment processing device. Also stored in this memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by processor 1301. Note that the computer readable storage medium can be either a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory; alternatively, it may be at least one computer-readable storage medium located remotely from the aforementioned processor.

In one embodiment, the payment processing device may be the payment terminal 101 or the backend service device 102 shown in fig. 1 a; the computer-readable storage medium having one or more instructions stored therein; loading and executing, by processor 1301, one or more instructions stored in a computer-readable storage medium to implement the corresponding steps in the payment processing method embodiments described above; in particular implementations, one or more instructions in a computer-readable storage medium are loaded by processor 1301 and perform the steps of:

determining a target payment object from the at least two payment objects;

In one implementation, the at least two payment objects include a first payment object and a second payment object, and the one or more instructions in the computer readable storage medium are loaded by the processor 1301 and when executed perform the steps of collecting the biometric characteristics of the at least two payment objects, specifically:

In one implementation, one or more instructions in a computer-readable storage medium are loaded by processor 1301 and when executed to determine a target payment object from at least two payment objects, specifically perform the steps of:

outputting a biometric of each of the at least two payment objects;

In one implementation, the payment policy includes a visual recognition algorithm, and one or more instructions in the computer readable storage medium are loaded by the processor 1301 and when executing a scoring process on the biometric of each of the at least one payment object according to the payment policy, the following steps are specifically performed to obtain a score for each payment object:

In one implementation, the payment policy includes a behavior prediction rule, and one or more instructions in the computer readable storage medium are loaded by the processor 1301 and when executing a scoring process on the biometric of each of the at least one payment object according to the payment policy, the following steps are specifically performed to obtain a score for each payment object:

In one implementation, the payment policy includes a look-similarity rule, and one or more instructions in the computer-readable storage medium are loaded by the processor 1301 and when executing a scoring process on the biometric of each of the at least one payment object according to the payment policy, the following steps are specifically performed to obtain a score for each payment object:

Acquiring a reference biological characteristic of a reference object;

In one implementation, one or more instructions in the computer-readable storage medium are loaded by the processor 1301 and when executed to select a target payment object from at least one payment object based on the score of each payment object, specifically perform the steps of:

In one implementation, one or more instructions in a computer-readable storage medium are loaded by processor 1301 and further perform the steps of:

In the embodiment of the present application, based on the same inventive concept, the principle and the beneficial effect of the payment processing apparatus for solving the problem provided in the embodiment of the present application are similar to those of the payment processing method for solving the problem in the embodiment of the present application, and may refer to the principle and the beneficial effect of implementation of the method, and for brevity description, no further description is provided here.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the payment processing apparatus reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the payment processing apparatus performs the above-described payment processing method.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

The foregoing is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present disclosure, and all changes and substitutions are intended to be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A payment processing method, comprising:

determining a target payment object from the at least two payment objects;

acquiring object information of the target payment object according to the biological characteristics of the target payment object;

2. The method of claim 1, wherein the at least two payment objects include a first payment object and a second payment object, the acquiring the biometric of the at least two payment objects comprising:

sequentially collecting the biological characteristics of the first payment object and the biological characteristics of the second payment object within a set time range; or alternatively, the process may be performed,

3. The method of claim 1, wherein the determining a target payment object from the at least two payment objects comprises:

outputting a payment object selection list, wherein the payment object selection list comprises the collected biological characteristics of each payment object in the at least two payment objects;

and determining the selected payment object as the target payment object in response to a selection operation of the payment object selection list.

4. The method of claim 1, wherein the determining a target payment object from the at least two payment objects comprises:

outputting a biometric of each of the at least two payment objects;

if the confirmation operation of at least one payment object in the at least two payment objects is detected, scoring the biological characteristics of each payment object in the at least one payment object according to a payment strategy to obtain the score of each payment object;

And selecting a target payment object from the at least one payment object based on the score of each payment object.

5. The method of claim 4, wherein the payment policy includes a visual recognition algorithm, wherein scoring the biometric of each of the at least one payment object according to the payment policy to obtain the score for each payment object comprises:

and scoring the biological characteristics of each payment object in the at least one payment object by adopting the visual recognition algorithm to obtain the visual recognition score of each payment object.

6. The method of claim 4, wherein the payment policy includes behavior prediction rules, wherein scoring the biometric of each of the at least one payment object according to the payment policy to obtain the score for each payment object comprises:

performing feature recognition processing on the biological characteristics of each payment object in the at least one payment object to obtain an identifier of each payment object;

7. The method of claim 4, wherein the payment policy includes a look-like rule, wherein scoring the biometric of each of the at least one payment object according to the payment policy to obtain the score for each payment object comprises:

acquiring a reference biological characteristic of a reference object;

and obtaining the appearance similarity score of each payment object based on the appearance similarity between the biological characteristics of each payment object and the reference biological characteristics.

8. The method of any of claims 4-7, wherein selecting a target payment object from the at least one payment object based on the score of each payment object comprises:

9. The method of claim 1, wherein after the obtaining the object information of the target payment object according to the biometric feature of the target payment object, further comprising:

outputting a payment object confirmation interface, wherein the payment object confirmation interface comprises object information of the target payment object;

wherein the payment confirmation event comprises: and executing an event generated when the confirming operation is executed on the object information of the target payment object, and an event generated when the display time of the object information of the target payment object in the payment object confirming interface is longer than a time length threshold.

10. The method of claim 9, wherein the method further comprises:

displaying a score list in the payment object confirmation interface, wherein the score list comprises the score of each payment object;

When the target score in the score list is selected, taking the payment object corresponding to the target score as a target payment object, and displaying object information of the payment object corresponding to the target score in the payment object confirmation interface.

11. A payment processing apparatus, comprising:

a processing unit for determining a target payment object from the at least two payment objects;

the processing unit is further used for acquiring object information of the target payment object according to the biological characteristics of the target payment object;

the processing unit is further used for carrying out payment processing on the target order based on the object information of the target payment object.

12. A payment processing apparatus, comprising:

a processor adapted to execute a computer program;

a computer readable storage medium having stored therein a computer program which, when executed by the processor, implements a payment processing method as claimed in any one of claims 1 to 10.

13. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program adapted to be loaded by a processor and to perform a payment processing method according to any of claims 1-10.

14. A computer program product comprising computer instructions which, when executed by a processor, implement a payment processing method as claimed in any one of claims 1 to 10.