CN111667363A

CN111667363A - Bank account opening user double-recording risk identification method and device and computer equipment

Info

Publication number: CN111667363A
Application number: CN202010583433.XA
Authority: CN
Inventors: 熊玮
Original assignee: OneConnect Financial Technology Co Ltd Shanghai
Current assignee: OneConnect Smart Technology Co Ltd; OneConnect Financial Technology Co Ltd Shanghai
Priority date: 2020-06-23
Filing date: 2020-06-23
Publication date: 2020-09-15

Abstract

The invention discloses a method and a device for identifying double-recording risks of bank account opening users, computer equipment and a storage medium, and relates to artificial intelligence biological identification. The method realizes automatic data processing of the user account opening process based on the self-service handling terminal, and also adopts the micro-expression recognition technology to carry out risk grade recognition on the user account opening double-recording process, thereby not only improving the efficiency of user account opening double-recording and reducing the cost for realizing user double-recording, but also timely and accurately recognizing the user risk grade.

Description

Bank account opening user double-recording risk identification method and device and computer equipment

Technical Field

The invention relates to the technical field of artificial intelligence biological identification, in particular to a method and a device for identifying double-recording risks of bank account opening users, computer equipment and a storage medium.

Background

At present, account opening matters are handled under financial sales situations such as securities, insurance and the like, or the matters are handled through a manual matter window, and a salesman performs manual auditing and double recording processes to obtain user videos and user audios in the account opening process.

In the account opening double-recording process, the general flow is fixed, if the account opening double-recording of the user is carried out through a manual agent, the efficiency is low, the manual realization cost of the double-recording is high, and the risk level of the user cannot be identified in time.

Disclosure of Invention

The embodiment of the invention provides a bank account opening user double-recording risk identification method, a bank account opening user double-recording risk identification device, computer equipment and a storage medium, and aims to solve the problems that account opening items are handled under a financial sales scene, and double recording of a user account opening through a manual agent is low in efficiency, high in manual double recording implementation cost and incapable of being performed on the user in time.

In a first aspect, an embodiment of the present invention provides a method for identifying double-entry risks of bank account opening users, where the method includes:

if a current user head portrait sent by monitoring equipment is received, current user identification information corresponding to the current user head portrait is obtained;

judging whether the locally stored user identity information set stores user identity information which is the same as the current user identification information;

if the locally stored user identity information set stores user identity information which is the same as the current user identification information, acquiring corresponding target user identity information; the user identity information set comprises a plurality of pieces of user identity information, and each piece of user identity information comprises an identity unique identification code and a user VIP level;

acquiring the age of the current user according to the unique identity identification code corresponding to the identity information of the target user, switching a local user interaction interface to a corresponding current user display interface, and performing a first voice prompt for navigating the user to the local machine operation;

if an account opening item transaction instruction of the user is received, activating a camera and a microphone to record video and audio to obtain the video and the audio of the current user;

obtaining a micro-expression recognition result by carrying out micro-expression recognition on the current user video;

judging whether the recognition value corresponding to the micro-expression recognition result is smaller than a preset recognition value threshold value or not; and

and if the identification value corresponding to the micro-expression identification result is smaller than the identification value threshold value, performing a second voice prompt for prompting the user that the audit is not passed and manual handling is required.

In a second aspect, an embodiment of the present invention provides a double-entry risk identification apparatus for a bank account opening user, including:

the current user identification unit is used for acquiring current user identification information corresponding to the current user head portrait if the current user head portrait sent by the monitoring equipment is received;

a user identity information judging unit, configured to judge whether a user identity information set stored locally stores user identity information that is the same as the current user identification information;

the target information acquisition unit is used for acquiring corresponding target user identity information if the user identity information which is the same as the current user identification information is stored in a locally stored user identity information set; the user identity information set comprises a plurality of pieces of user identity information, and each piece of user identity information comprises an identity unique identification code and a user VIP level;

the first prompting unit is used for acquiring the age of the current user according to the unique identity identification code corresponding to the identity information of the target user, switching the local user interaction interface to the corresponding current user display interface and carrying out first voice prompt for navigating the user to the local machine operation;

the double-recording unit is used for activating the camera and the microphone to record video and audio to obtain the video and the audio of the current user if an account opening item handling instruction of the user is received;

the micro-expression recognition unit is used for carrying out micro-expression recognition on the current user video to obtain a micro-expression recognition result;

the recognition value comparison unit is used for judging whether the recognition value corresponding to the micro-expression recognition result is smaller than a preset recognition value threshold value or not; and

and the second prompting unit is used for prompting the user that the audit is not passed and the user needs to manually handle the micro-expression recognition result if the recognition value corresponding to the micro-expression recognition result is smaller than the recognition value threshold value.

In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the double-entry risk identification method for bank account opening users according to the first aspect.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the method for identifying a double-recording risk of a bank account opening user according to the first aspect.

The embodiment of the invention provides a bank account opening user double-recording risk identification method, a device, computer equipment and a storage medium, when a user arrives at a self-service account opening terminal at a bank hall to open an account by self, double recording is carried out to obtain a current user video and a current user audio, and whether the self-service account is successfully handled is determined by a micro-expression identification result based on the micro-expression identification result corresponding to the current user video. The method realizes automatic data processing of the user account opening process based on the self-service handling terminal, and also adopts the micro-expression recognition technology to carry out risk grade recognition on the user account opening double-recording process, thereby not only improving the efficiency of user account opening double-recording and reducing the cost for realizing user double-recording, but also timely and accurately recognizing the user risk grade.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a double-recording risk identification method for a bank account opening user according to an embodiment of the present invention;

fig. 2 is a schematic flow chart of a double-entry risk identification method for a bank account opening user according to an embodiment of the present invention;

fig. 3 is a schematic sub-flow diagram of a double-entry risk identification method for a bank account opening user according to an embodiment of the present invention;

fig. 4 is a schematic block diagram of a double-recording risk identification device for a bank account opening user according to an embodiment of the present invention;

fig. 5 is a schematic block diagram of a sub-unit of a double-recording risk identification device for a bank account opening user according to an embodiment of the present invention;

FIG. 6 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Referring to fig. 1 and fig. 2, fig. 1 is a schematic view of an application scenario of a double-recording risk identification method for a bank account opening user according to an embodiment of the present invention; fig. 2 is a schematic flow chart of a method for identifying double-record risks of a bank account opening user according to an embodiment of the present invention, where the method is applied to a self-service terminal placed in a lobby of a notarization department, and is executed by application software installed in the self-service terminal placed in the lobby of the notarization department.

As shown in fig. 2, the method includes steps S101 to S106.

S101, if a current user head portrait sent by a monitoring device is received, current user identification information corresponding to the current user head portrait is obtained.

In this embodiment, the technical solution is described in a double recording scenario of bank account opening. For example, the technical solution is described more specifically in the context of a self-service terminal at a bank lobby. When a user arrives at an entrance of a bank business hall and a monitoring device (such as a monitoring camera) collects a current user image, the current user image is sent to a plurality of self-service handling terminals deployed in the bank business hall.

When the monitoring equipment at the entrance of the bank business hall collects the head portrait of the current user of the user entering the bank business hall at the moment, the head portrait of the current user sent by the monitoring equipment is received through the self-service terminal and is sent to the server in communication connection with the self-service terminal. And acquiring current user identification information corresponding to the head portrait of the current user by using an image identification model in the server and a stored user identity template. The identification of the user through the head portrait of the current user is a mature technology in the prior art, and is not discussed here.

If a plurality of self-service handling terminals are deployed in the bank business hall, any one of the self-service handling terminals in the idle state can receive the current user head portrait sent by the monitoring equipment. After the idle self-service terminal receives the head portrait of the current user sent by the monitoring equipment and sends the head portrait to the server, the user identity can be identified and corresponding current user identification information can be obtained.

S102, judging whether the locally stored user identity information set stores the user identity information which is the same as the current user identification information.

In this embodiment, after the current user identification information is obtained through the identification of the head portrait of the current user, in order to determine whether the current user is an old user in the current row, at this time, whether the user identification information that is the same as the current user identification information is stored in the locally stored user identification information set may be retrieved. When the locally stored user identity information set stores the user identity information which is the same as the current user identification information, the user is represented as an old user of the current line; and when the locally stored user identity information set does not store the user identity information which is the same as the current user identification information, the user is not an old user but a new user in the current line.

S103, if the locally stored user identity information set stores user identity information which is the same as the current user identification information, acquiring corresponding target user identity information; the user identity information set comprises a plurality of pieces of user identity information, and each piece of user identity information comprises an identity unique identification code and a user VIP level.

In this embodiment, a plurality of self-service handling terminals in a bank business hall store user identity information sets, and the user identity information sets are generally sent to respective self-service handling terminals by a server periodically for data updating. At this time, the self-service processing terminal which acquires the identification information of the current user compares the unique identification code corresponding to the identification information of the current user with the unique identification codes corresponding to the identification information of each user in the locally stored user identification information set, judges whether the identification information of the current user is the same as the unique identification code in the locally stored user identification information set or not, if the unique identification code corresponding to the identification information of the current user is the same as the unique identification code of one piece of user identification information in the locally stored user identification information set, the user is an old client of the bank, and the VIP level of the user can be determined according to the historical item data of the user.

And S104, acquiring the age of the current user according to the unique identity identification code corresponding to the identity information of the target user, switching the local user interaction interface to the corresponding current user display interface, and carrying out first voice prompt for navigating the user to the local machine operation.

In this embodiment, after receiving the head portrait of the current user sent by the monitoring device and determining the unique identity identifier of the current user by the self-service terminal in the current idle state (for example, the unique identity identifier is a user identification number), the current user age may be generally calculated according to the unique identity identifier, so as to switch the user interaction page of the self-service terminal to the user interaction interface suitable for the user in the age group.

For example, a first user interaction interface may be set for a group of 18-40 years old users (generally, a full-event function is displayed on the self-service terminal, and a font size display is set with a font size of two), a second user interaction interface may be set for a group of 41-60 years old users (generally, a partial non-core event function simplified display is performed on the self-service terminal, and a font size display is set with a font size of one), and a third user interaction interface may be set for a group of 60 years old or older (generally, a few core function displays on the self-service terminal, and a font size display is set with a font size of one). Through the display of the hierarchical user interaction interfaces, the actual requirements of users in corresponding age groups can be fully considered, and therefore the pages can be displayed more accurately.

And S105, if an account opening item transaction instruction of the user is received, activating a camera and a microphone to record video and audio to obtain the video and the audio of the current user.

In this embodiment, when the user performs double recording caused by opening an account on the self-service terminal of the bank business hall, the whole process of broadcasting a plurality of questions to the user and answering the questions by the user can be recorded by video and audio, and double recording in the whole processing process is also realized. The current user video and the current user audio obtained in the double recording process can be used as a basis for subsequently identifying the user micro-expression data.

And S106, carrying out micro-expression recognition on the current user video to obtain a micro-expression recognition result.

In this embodiment, in order to more objectively determine the user state (that is, the micro expression state) of the user in the bank account opening transaction process, at this time, the micro expression recognition result is obtained by performing micro expression recognition on the current user video, and the micro expression recognition result can more accurately determine whether the user is suspected of fraud in the bank account opening transaction process.

In one embodiment, as shown in fig. 3, step S106 includes:

s1061, acquiring an image frame containing a micro expression in a video image sequence of the current user video through an optical flow method;

s1062, acquiring continuous multi-frame images with the number equal to the number of the empirical frames from the image frames containing the micro expression according to a preset empirical frame value to form a micro expression sequence;

s1063, calling a pre-constructed weight calculation layer to calculate the weight feature vector of each frame of image in the micro expression sequence so as to obtain the image feature vector of each frame of image combined with the weight value;

s1064, summing the image feature vectors of each frame of image combined with the weighted values to obtain a comprehensive image feature vector corresponding to the current user video;

and S1065, inputting the comprehensive image feature vector to a pre-trained convolutional neural network to obtain a micro expression recognition result.

In this embodiment, in the process of transaction of the account opening items of the user bank, the self-service transaction terminal starts a camera to collect the video of the current user and locally perform micro-expression recognition. After the self-service terminal collects the current user video, the image frame containing the micro expression is obtained so as to perform subsequent micro expression recognition.

The image frames containing the micro-expressions in the video image sequence of the current user video can be obtained through an optical flow method or a space-time local texture operator.

In this embodiment, any suitable feature extraction method may be specifically selected and used to extract the image frames of the micro-expressions included in the video image sequence. For example, optical flow-based feature extraction or LBP-TOP operator-based feature extraction may be used:

the optical flow algorithm is used for estimating the optical flow in a video image sequence under a certain constraint condition so as to identify the fine motion of the face of a client and realize the feature extraction of the micro expression. The LBP-TOP operator (i.e. the empty local texture) is developed on the basis of a local binary pattern (LBP operator) to reflect the characteristics of the spatial distribution of pixels in the video image sequence. Simply speaking, on the basis of an LBP operator, a dimension in time is newly added, so that the change characteristics of each pixel point in a video image sequence along with the time can be extracted, and the fine expression change of the face of a client is identified.

In an embodiment, step S1061 includes:

acquiring speed vector characteristics corresponding to each pixel point of a video image sequence of the current user video;

and if the speed vector characteristics of at least one frame of image in the video image sequence do not keep continuously changing, forming an image frame containing the micro expression by the corresponding pictures.

In this embodiment, when a person's eye observes a moving object, the scene of the object forms a series of continuously changing images on the retina of the person's eye, and this series of continuously changing information continuously "flows" through the retina (i.e., the image plane) as if it were a "stream" of light, and is therefore referred to as an optical flow. The optical flow expresses changes in the image, containing information of the motion of the object, which can be used to determine the motion of the object. Three elements of optical flow: one is the motion velocity field, which is a necessary condition for forming optical flow; the part with optical characteristics, such as gray pixel points, can carry motion information; and thirdly, the imaging projection is from the scene to the image plane and can thus be observed.

Defining the optical flow is based on points, and specifically, assuming that (u, v) is the optical flow of the image point (x, y), the (x, y, u, v) is referred to as an optical flow point. The collection of all optical flow points is called an optical flow field. When an object with optical properties moves in three-dimensional space, a corresponding image motion field, or image velocity field, is formed at the image plane. In an ideal case, the optical flow field corresponds to a motion field.

Each pixel in the image is assigned a velocity vector, thus forming a motion vector field. According to the speed vector characteristics of each pixel point, the image can be dynamically analyzed. If there is no moving object in the image, the optical flow vector is continuously varied over the entire image area. When a moving object exists in the image (when the user has a micro expression, the face moves, which is equivalent to the moving object), the target and the background move relatively. The velocity vector formed by the moving object is different from the velocity vector of the background, so that the position of the moving object can be calculated. And preprocessing the video image sequence of the current user video by an optical flow method to obtain an image frame containing the micro expression in the video image sequence of the current user video.

The value of the empirical frame is denoted as N, which is an empirical value and can be set by a technician according to the needs of actual situations. That is, it is ensured that a complete process of the micro expression from the beginning, peak to the end is recorded in the N frames of images.

The relation between the image frames in the micro-expression sequence (i.e. the time domain information of the micro-expression image sequence) can be represented by the difference of the weight values. For example, in a smiling sequence, several image frames always appear jointly, and the time domain information of the sequence can be obtained by increasing the weight of the jointly appearing image frames.

In order to assign a weight value to each frame of image in the micro expression sequence, a pre-constructed weight calculation layer is required to be called to calculate a weight feature vector of each frame of image in the micro expression sequence.

In one embodiment, step S1063 includes:

acquiring a picture characteristic vector corresponding to each frame of picture in the micro expression sequence and a picture characteristic vector set corresponding to each frame of picture; the picture feature vector set corresponding to the ith frame of image in the micro expression sequence consists of picture feature vectors corresponding to other frames of images except the ith frame of image in the micro expression sequence, the value range of i is [1, N ], and N is an empirical frame value;

acquiring similarity values between the picture characteristic vector of each frame of image in the micro expression sequence and the picture characteristic vectors of other frames of images to obtain a similarity value set corresponding to each frame of image; similarity values between the picture feature vector of the ith frame of image in the micro expression sequence and the picture feature vectors of other frames of images form a similarity value set of the ith frame of image;

normalizing the similarity value sets respectively corresponding to each frame of image in the micro expression sequence to obtain normalized similarity value sets respectively corresponding to each frame of image;

and acquiring a weight characteristic vector corresponding to each frame of image according to the normalized similarity value set and the image characteristic vector set corresponding to each frame of image so as to obtain an image characteristic vector combining the weight value of each frame of image.

In this embodiment, since each frame of image in the micro expression sequence is initially without a weight value, in order to obtain the weight value of each frame of image, the following process may be performed:

1) acquiring a picture characteristic vector corresponding to each frame of image in the micro expression sequence, and specifically inputting each frame of image into a trained convolutional neural network to obtain a picture characteristic vector corresponding to each frame of image; then obtaining a picture characteristic vector set corresponding to each frame of image, wherein the picture characteristic vector set corresponding to the ith frame of image in the micro expression sequence consists of picture characteristic vectors corresponding to other frames of images except the ith frame of image in the micro expression sequence, the value range of i is [1, N ], and N is an empirical frame value;

2) recording the ith frame image in the N frame images of the micro expression sequence as N_iFirstly, inputting the picture characteristic vector corresponding to one frame of image into a weight calculation layer to calculate the similarity between the picture characteristic vector of the frame of image and the picture characteristic vectors of the rest N-1 frames of images in the micro expression sequence, thereby obtaining a similarity value set corresponding to each frame of image; and the similarity values between the picture characteristic vector of the ith frame of image in the micro expression sequence and the picture characteristic vectors of other frames of images form a similarity value set of the ith frame of image. The similarity can be evaluated in any suitable manner, such as by using the vector dot product between the image feature vectors of the two images, the cosine similarity or by introducing a new neural network;

3) normalizing the similarity value sets respectively corresponding to each frame of image in the micro expression sequence obtained by calculation to obtain normalized similarity value sets respectively corresponding to each frame of image;

4) because each frame of image corresponds to one normalized similarity value set, each normalized similarity value in the normalized similarity value set is multiplied by the image feature vector of the corresponding frame and then summed, and the image feature vector of the combined weight value corresponding to each frame of image is obtained.

Through the weight calculation layer, the internal relation between different image frames in the micro-expression image sequence can be obtained through mining. That is, some closely related image frames may have a significantly higher weight value than other image frames, so that more attention can be paid to the recognition process of the micro-expression.

In an embodiment, the step of obtaining the weight feature vector corresponding to each frame of image according to the normalized similarity value set and the image feature vector set corresponding to each frame of image to obtain the image feature vector of each frame of image combined with the weight value includes:

multiplying each normalized similarity value in the normalized similarity value set of the ith frame image by the corresponding picture feature vector in the picture feature vector set of the ith frame image, and then summing to obtain the weight feature vector corresponding to the ith frame image so as to obtain the corresponding image feature vector combined with the weight value of the ith frame image.

The image feature vectors of the ith frame image obtained in this way and corresponding combined with the weighted values fully take the internal relation between different image frames into consideration.

After the image feature vectors of the combination weight values of each frame of image are obtained, in order to comprehensively consider the micro-expression recognition results corresponding to the images of the frames, the image feature vectors of the combination weight values of each frame of image can be summed to obtain the comprehensive image feature vector corresponding to the current user video, and then the comprehensive image feature vector is used as a recognition vector to perform micro-expression recognition.

And after the comprehensive image characteristic vector is obtained, the comprehensive image characteristic vector represents the comprehensive picture characteristic vector corresponding to the N frames of images in the video image sequence, and the comprehensive image characteristic vector is input into a convolutional neural network used by the weight calculation layer, so that a micro-expression recognition result can be obtained.

In one embodiment, step S1065 includes:

and inputting the comprehensive image feature vector to a softmax layer of a pre-trained convolutional neural network to obtain a micro-expression recognition result.

In this embodiment, since the convolutional layer, the pooling layer, and the full-link layer are already used in the convolutional neural network used in the weight calculation layer, the corresponding picture feature vector is obtained, and at this time, after the synthetic image feature vector is obtained, the synthetic image feature vector may be input to the softmax layer of the convolutional neural network, so as to obtain a final micro-expression recognition result. Specifically, the probability that the micro-expression belongs to each category is obtained, and the category with the highest probability is selected as the micro-expression recognition result of the micro-expression sequence.

And S107, judging whether the recognition value corresponding to the micro-expression recognition result is smaller than a preset recognition value threshold value.

In this embodiment, an identification value threshold (e.g. 0.6) may be preset, and at this time, the identification value corresponding to the micro-expression identification result is compared with the identification value threshold, so as to further determine whether the user is suspected of fraud in the process of handling the bank account opening transaction.

And S108, if the identification value corresponding to the micro-expression identification result is smaller than the identification value threshold value, performing a second voice prompt for prompting that the user fails the audit and needs to go to manual handling.

In this embodiment, after the identification value corresponding to the microexpression identification result is compared with the identification value threshold, it is determined that the identification value corresponding to the microexpression identification result is lower than the identification value threshold, which indicates that the account opening process is in doubt (i.e. a higher risk is caused if the account opening of the user is successfully approved through the self-service terminal), and for the account opening user in doubt, the user is prompted to enter a manual service area for manual account opening verification operation. At the moment, the self-service terminal broadcasts a second voice prompt for prompting the user that the audit is not passed and the user needs to go to manual handling.

In an embodiment, as shown in fig. 2, step S108 is followed by:

s109, acquiring the estimated end time corresponding to each artificial customer service terminal, and sending the target user ranking number generated according to the target user identity information to the artificial customer service terminal with the estimated end time as the earliest end time.

In this embodiment, when a user goes to a manual window to perform manual account opening process audit, the user generally needs to wait in a queue, the existing queuing mechanism is that the user swipes an identity card on a queuing machine to obtain a queuing number, at this time, a queuing number pool is provided, and once a certain manual window is in an idle state and can continue to provide manual service, the queuing number ranked first is taken out from the queuing number pool to call the corresponding user to handle items. However, the number calling method does not fully consider the situation that the account opening client still needs to wait for a long time to manually handle. At this time, the estimated end time corresponding to each artificial customer service terminal can be directly obtained, so that the target user ranking generated according to the target user identity information is sent to the artificial customer service terminal with the estimated end time being the earliest end time, and the artificial customer service terminal which can be in an idle state at the earliest time is automatically selected, so that the user waiting time is reduced, and the item handling efficiency is improved.

In one embodiment, step S109 includes:

acquiring currently transacting user information corresponding to each manual service terminal, and finishing time according to the currently transacting user information on each manual service terminal and historical matters corresponding to the currently transacting user information;

and acquiring the estimated ending time corresponding to the user information currently transacted on each manual service terminal according to the historical item transaction duration corresponding to the user information currently transacted on each manual service terminal and the item transaction starting time corresponding to the user information currently transacted.

In this embodiment, the information of the currently transacted user corresponding to each manual service terminal of each current manual window needs to be acquired through the self-service transaction terminal, at this time, the information of the name of the currently transacted item, the average transaction duration of historical items, and the like on each manual service terminal can be clearly acquired, and the transaction end time of the item of the user on each manual service terminal is estimated.

For example, there are 4 manual windows in the bank business hall, each manual window is provided with a manual client terminal (i.e. an intelligent terminal used by window personnel), and each manual service terminal can feed back corresponding information of the currently transacted user to the self-service transaction terminal.

At the current time of 15:00, the currently transacted user information fed back by the manual service terminal No. 1 is user A, the transaction duration of the historical matters corresponding to the user A is 20 minutes, the starting time of the user A for starting transaction at the manual service terminal No. 1 is 14:50, and the estimated ending time corresponding to the currently transacted user information on the manual service terminal No. 1 is 15: 10.

The user information currently transacted fed back by the number 2 manual service terminal is a user B, the transaction duration of the historical transaction corresponding to the user B is 25 minutes, the starting time of the user B starting transaction at the number 2 manual service terminal is 14:40, and therefore the estimated ending time corresponding to the user information currently transacted at the number 2 manual service terminal is 15: 05.

The user information currently transacted fed back by the number 3 manual service terminal is a user C, the transaction duration of the historical transaction corresponding to the user C is 40 minutes, the starting time of the user C for starting transaction at the number 3 manual service terminal is 14:50, and therefore the estimated ending time corresponding to the user information currently transacted at the number 3 manual service terminal is 15: 30.

The user information currently transacted fed back by the number 4 manual service terminal is a user D, the transaction duration of the historical transaction corresponding to the user D is 15 minutes, the starting time of the user D for starting transaction at the number 4 manual service terminal is 14:53, and therefore the estimated ending time corresponding to the user information currently transacted at the number 4 manual service terminal is 15: 08.

The 4 exemplary manual service terminals correspond to an estimated end time, and the estimated end time corresponding to the currently transacted user information on the manual service terminal # 2 is the earliest estimated end time, and at this time, the target user ranking number generated according to the identity information of the target user can be sent to the manual service terminal # 2. Once the corresponding user on the number 2 manual customer service terminal transacts the item, the number of the target user can be called.

In an embodiment, step S107 is followed by:

and if the identification value corresponding to the micro-expression identification result is greater than or equal to the identification value threshold value, performing a third voice prompt for prompting the user to electronically sign and determining to finish the transaction.

In this embodiment, the micro-expression recognition result is greater than or equal to the preset recognition value threshold, which indicates that the user account opening data has no question, and at this time, the user is prompted to use an electronic signature mode, leave a relevant certificate, and determine to end the transaction of the account opening item.

In an embodiment, step S108 is followed by:

and uploading the summary information corresponding to the local storage paths of the current user video and the current user audio to a block chain network.

In this embodiment, the blockchain corresponding to the blockchain network is a novel application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.

In order to retain the double-recording evidence, the summary information corresponding to the local storage path of the current user video and the current user audio can be retained in the blockchain network for a long time, so that subsequent traceability query is facilitated.

The method realizes automatic data processing of the user account opening process based on the self-service handling terminal, and also adopts the micro-expression recognition technology to carry out risk grade recognition on the user account opening double-recording process, thereby not only improving the efficiency of user account opening double-recording and reducing the cost for realizing user double-recording, but also timely and accurately recognizing the user risk grade.

The embodiment of the invention also provides a double-recording risk identification device for the bank account opening user, which is used for executing any embodiment of the double-recording risk identification method for the bank account opening user. Specifically, referring to fig. 4, fig. 4 is a schematic block diagram of a risk identification apparatus for double entry of a bank account opening user according to an embodiment of the present invention. The bank account opening user double-recording risk recognition device 100 may be configured in a self-service transaction terminal placed in a bank business hall, and the self-service transaction terminal may be understood as a local server.

As shown in fig. 4, the double-recording risk identification apparatus 100 for bank account opening users includes: a current user identification unit 101, a user identity information judgment unit 102, a target information acquisition unit 103, a first prompting unit 104, a double recording unit 105, a micro expression identification unit 106, an identification value comparison unit 107, and a second prompting unit 108.

And the current user identification unit 101 is configured to, if a current user avatar sent by the monitoring device is received, obtain current user identification information corresponding to the current user avatar.

The user identity information determining unit 102 is configured to determine whether a locally stored user identity information set stores user identity information that is the same as the current user identification information.

A target information obtaining unit 103, configured to obtain corresponding target user identity information if the user identity information set stored locally stores user identity information that is the same as the current user identification information; the user identity information set comprises a plurality of pieces of user identity information, and each piece of user identity information comprises an identity unique identification code and a user VIP level.

The first prompting unit 104 is configured to obtain a current user age according to the unique identity identifier corresponding to the target user identity information, so as to switch the local user interaction interface to the corresponding current user display interface, and perform a first voice prompt for navigating the user to the local machine operation.

And a double recording unit 105, configured to activate a camera and a microphone to perform video and audio double-item recording if an account opening transaction instruction of the user is received, so as to obtain a current user video and a current user audio.

And a micro-expression recognition unit 106, configured to obtain a micro-expression recognition result by performing micro-expression recognition on the current user video.

In one embodiment, as shown in fig. 5, the micro expression recognition unit 106 includes:

a microexpression image frame acquiring unit 1061, configured to acquire, by using an optical flow method, an image frame including a microexpression in a video image sequence of the current user video;

a micro-expression sequence acquiring unit 1062, configured to acquire, according to a preset empirical frame value, a number of consecutive multi-frame images equal to the empirical frame value from image frames including a micro expression to form a micro-expression sequence;

a weight image feature vector obtaining unit 1063, configured to invoke a pre-constructed weight calculation layer to calculate a weight feature vector of each frame of image in the micro expression sequence, so as to obtain an image feature vector of each frame of image in combination with a weight value;

the comprehensive image feature vector acquisition unit 1064 is configured to sum the image feature vectors of the combination weight values of each frame of image to obtain a comprehensive image feature vector corresponding to the current user video;

and a micro expression recognition result obtaining unit 1065, configured to input the comprehensive image feature vector to a pre-trained convolutional neural network, so as to obtain a micro expression recognition result.

In one embodiment, the micro-expression image frame acquiring unit 1061 includes:

the speed vector feature acquisition unit is used for acquiring speed vector features corresponding to all pixel points of a video image sequence of the current user video;

and the target image frame acquisition unit is used for forming an image frame containing the micro expression by corresponding pictures if the speed vector characteristics of at least one frame of image in the video image sequence do not continuously change.

In one embodiment, the weighted image feature vector obtaining unit 1063 includes:

the image characteristic vector set acquisition unit is used for acquiring an image characteristic vector corresponding to each frame of image in the micro expression sequence and an image characteristic vector set corresponding to each frame of image; the picture feature vector set corresponding to the ith frame of image in the micro expression sequence consists of picture feature vectors corresponding to other frames of images except the ith frame of image in the micro expression sequence, the value range of i is [1, N ], and N is an empirical frame value;

a similarity value set obtaining unit, configured to obtain similarity values between the picture feature vector of each frame of image in the micro expression sequence and the picture feature vectors of other frames of images, so as to obtain a similarity value set corresponding to each frame of image; similarity values between the picture feature vector of the ith frame of image in the micro expression sequence and the picture feature vectors of other frames of images form a similarity value set of the ith frame of image;

the normalization unit is used for normalizing the similarity value sets respectively corresponding to each frame of image in the micro expression sequence to obtain normalized similarity value sets respectively corresponding to each frame of image;

and the weight characteristic vector acquisition unit is used for acquiring the weight characteristic vector corresponding to each frame of image according to the normalized similarity value set and the image characteristic vector set corresponding to each frame of image so as to obtain the image characteristic vector of each frame of image combined with the weight value.

In an embodiment, the weight feature vector obtaining unit is further configured to:

In one embodiment, the micro expression recognition result obtaining unit 1065 includes:

and the Softmax classification unit is used for inputting the comprehensive image feature vector to a Softmax layer of a pre-trained convolutional neural network to obtain a micro-expression recognition result.

An identification value comparing unit 107, configured to determine whether an identification value corresponding to the micro-expression recognition result is smaller than a preset identification value threshold.

And the second prompting unit 108 is configured to perform a second voice prompt for prompting the user that the audit is not passed and manual handling is required if the identification value corresponding to the micro-expression identification result is smaller than the identification value threshold.

In one embodiment, as shown in fig. 4, the double-recording risk identification apparatus 100 for bank account opening users further includes:

and the target user ranking number sending unit 109 is configured to obtain the estimated end time corresponding to each artificial customer service terminal, and send the target user ranking number generated according to the target user identity information to the artificial customer service terminal with the estimated end time being the earliest end time.

In an embodiment, the target user ranking number sending unit 109 includes:

a historical item handling time length obtaining unit, configured to obtain current handling user information corresponding to each manual service terminal, and handle a historical item handling time length corresponding to the current handling user information on each manual service terminal;

and the estimated end time acquiring unit is used for acquiring the estimated end time corresponding to the user information currently transacted on each manual service terminal according to the historical item transaction duration corresponding to the user information currently transacted on each manual service terminal and the item transaction start time corresponding to the user information currently transacted.

In an embodiment, the apparatus 100 for identifying risks of double entry of bank account opening user further includes:

and the third prompting unit is used for prompting the user to electronically sign and determining to finish the transaction if the identification value corresponding to the micro-expression identification result is greater than or equal to the identification value threshold value.

and the data uplink unit is used for uploading the summary information corresponding to the local storage paths of the current user video and the current user audio to a block chain network.

The device realizes automatic data processing of the user account opening process based on the self-service handling terminal, and also adopts the micro-expression recognition technology to carry out risk grade recognition on the user account opening double-recording process, thereby not only improving the efficiency of user account opening double-recording, reducing the cost for realizing user double-recording, but also identifying the user risk grade timely and accurately.

The bank account opening user double-recording risk identification device can be implemented in the form of a computer program, and the computer program can be run on a computer device as shown in fig. 6.

Referring to fig. 6, fig. 6 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 500 is a server, and the server may be an independent server or a server cluster composed of a plurality of servers.

Referring to fig. 6, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032, when executed, cause the processor 502 to perform a bank account opening user double entry risk identification method.

The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be enabled to execute the bank account opening user double-recording risk identification method.

The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

The processor 502 is configured to run the computer program 5032 stored in the memory, so as to implement the bank account opening user double-record risk identification method disclosed in the embodiment of the present invention.

Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 6 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 6, and are not described herein again.

It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by the processor, implements the bank account opening user double-recording risk identification method disclosed by the embodiment of the invention.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A double-recording risk identification method for bank account opening users is characterized by comprising the following steps:

2. The method for recognizing double-recording risks of bank account opening users according to claim 1, wherein after a second voice prompt for prompting the user that the audit is not passed and the manual transaction is required is performed if the recognition value corresponding to the micro-expression recognition result is smaller than the recognition value threshold, the method further comprises:

and acquiring the estimated end time corresponding to each artificial customer service terminal so as to send the target user ranking number generated according to the target user identity information to the artificial customer service terminal with the estimated end time as the earliest end time.

3. The method for identifying double-entry risks of bank account opening users according to claim 2, wherein the step of obtaining the estimated end time corresponding to each artificial customer service terminal comprises the following steps:

4. The method for identifying double-record risks of bank account opening users according to claim 1, wherein after judging whether the identification value corresponding to the micro-expression identification result is smaller than a preset identification value threshold, the method further comprises:

5. The double-recording risk identification method for bank account opening users according to claim 1, wherein the obtaining of the micro-expression identification result by performing micro-expression identification on the current user video comprises:

acquiring an image frame containing a micro expression in a video image sequence of the current user video through an optical flow method;

acquiring continuous multi-frame images with the number equal to the number of the experience frames from image frames containing the micro-expressions according to a preset experience frame value to form a micro-expression sequence;

calling a pre-constructed weight calculation layer to calculate the weight characteristic vector of each frame of image in the micro expression sequence so as to obtain the image characteristic vector of each frame of image combined with the weight value;

summing the image feature vectors of each frame of image combined with the weighted values to obtain a comprehensive image feature vector corresponding to the current user video;

and inputting the comprehensive image feature vector to a pre-trained convolutional neural network to obtain a micro-expression recognition result.

6. The method for identifying double-recording risks of bank account opening users according to claim 5, wherein the step of acquiring the image frames containing the micro-expressions in the video image sequence of the current user video through an optical flow method comprises the following steps:

7. The method for identifying the double-recording risk of the bank account opening user according to claim 5, wherein the step of calling a pre-constructed weight calculation layer to calculate the weight feature vector of each frame of image in the micro expression sequence so as to obtain the image feature vector of each frame of image combined with the weight value comprises the following steps:

8. A bank account opening user double-recording risk recognition device is characterized by comprising:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the bank account opening user double-entry risk identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to execute the bank account opening user double-entry risk identification method according to any one of claims 1 to 7.