CN112183270A

CN112183270A - Method and device for optimizing shooting parameters of identity authentication and electronic equipment

Info

Publication number: CN112183270A
Application number: CN202010986190.4A
Authority: CN
Inventors: 颜林
Original assignee: Alipay Labs Singapore Pte Ltd
Current assignee: Alipay Labs Singapore Pte Ltd
Priority date: 2020-09-18
Filing date: 2020-09-18
Publication date: 2021-01-05

Abstract

The embodiment of the specification discloses an identity authentication shooting parameter optimization method and device and electronic equipment. The method comprises the following steps: acquiring a face image of a target user and a certificate image of the target user; performing identity verification on the target user based on the face image of the target user and the certificate image of the target user; if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during the identity authentication; and inputting the behavior characteristic data of the target user and the first shooting parameters into a prediction model with a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data of the target user and the first shooting parameters.

Description

Method and device for optimizing shooting parameters of identity authentication and electronic equipment

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for optimizing shooting parameters for authentication, and an electronic device.

Background

Currently, in the financial field, more and more financial companies or institutions choose an online authentication method to authenticate the identity of a user, proving that "you are you". The authentication mode adopts a mode of shooting the identity document of the user and collecting the face image of the user for matching authentication to verify whether the face in the face image of the user is consistent with the face on the identity document of the user and whether the identity document of the user is a real document, thereby proving that 'you are you'. This type of product is also called an electronic knock-out Customer (eKYC) system of enterprises.

The eKYC generally needs a user to shoot an identity document and collect a face image of the user in the practical application process. However, in the process of taking an identity document, it is often difficult for a user to take an identity document and a face picture with good image quality at one time due to subjective (user proficiency) or objective (influence of light and environment). In this case, the user is required to take the identity document and the face image several times until a picture is taken with an image quality that meets the recognition capability of the system. In the process, if the user does not pass through the identity document and the face image shot for many times, the user identity authentication fails, and the user experience is further influenced.

Disclosure of Invention

The embodiment of the specification aims to provide an identity authentication shooting parameter optimization method, an identity authentication shooting parameter optimization device and electronic equipment, so as to solve the problem that in the prior art, when a user performs identity authentication, the user usually fails to perform identity authentication because the user does not pass through shooting identity documents and face images for many times, and further user experience is influenced.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

in a first aspect, a method for optimizing shooting parameters for identity authentication is provided, including:

acquiring a face image of a target user and a certificate image of the target user;

performing identity verification on the target user based on the face image of the target user and the certificate image of the target user;

if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during the identity authentication;

inputting the behavior characteristic data and the first shooting parameters of the target user into a prediction model with a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data and the first shooting parameters of the target user so as to optimize the shooting parameters of the target user during identity verification based on the target shooting parameters;

the prediction models of the parameters at least comprise a prediction model of a certificate quality threshold, a prediction model of a face quality threshold and a prediction model of a comparison threshold between a certificate picture and a face picture; the prediction model of the target parameter is obtained by training with the parameter value of the target parameter during multiple identity verifications as a label and with other parameters except the target parameter in the multiple parameters as features, wherein the target parameter is any one of the multiple parameters.

In a second aspect, an apparatus for optimizing shooting parameters for identity authentication is provided, including:

the image acquisition module is used for acquiring a face image of a target user and a certificate image of the target user;

the identity authentication module is used for authenticating the identity of the target user based on the face image of the target user and the certificate image of the target user;

the parameter acquisition module is used for acquiring behavior characteristic data of the target user in a preset time period and first shooting parameters when the target user performs identity authentication if the identity authentication of the target user fails based on the face image and the certificate image of the target user;

the parameter prediction module is used for inputting the behavior characteristic data of the target user and the first shooting parameters into prediction models of a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data of the target user and the first shooting parameters so as to optimize the shooting parameters of the target user during identity verification based on the target shooting parameters;

In a third aspect, an electronic device is provided, which includes:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

In a fourth aspect, a computer-readable storage medium is presented, the computer-readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to:

The embodiment of the specification can achieve at least the following technical effects by adopting the technical scheme:

by adopting the method provided by the embodiment of the specification, when the user is authenticated, the face image of the target user and the certificate image of the target user can be obtained; performing identity verification on the target user based on the face image of the target user and the certificate image of the target user; if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during the identity authentication; and finally, inputting the behavior characteristic data and the first shooting parameters of the target user into a prediction model with a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data and the first shooting parameters of the target user so as to optimize the shooting parameters of the target user during identity verification based on the target shooting parameters.

The prediction models of the parameters at least comprise a prediction model of a certificate quality threshold, a prediction model of a face quality threshold and a prediction model of a comparison threshold between a certificate picture and a face picture; the prediction model of the target parameter is obtained by training with the parameter value of the target parameter during multiple identity verifications as a label and with other parameters except the target parameter in the multiple parameters as features, wherein the target parameter is any one of the multiple parameters. The method can model the main parameter factors in the authentication process to obtain the prediction model of the certificate quality threshold, the prediction model of the face quality threshold and the prediction model of the comparison threshold between the certificate picture and the face picture, so that the optimal values of the main parameters in the authentication process can be predicted in a machine learning mode, and further, each main parameter in the authentication process can reach a state with better user experience, thereby improving the passing rate of the user for authentication and the user experience.

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the document and not to limit the document inappropriately. In the drawings:

fig. 1 is a schematic implementation flow diagram of an authentication shooting parameter optimization method provided in an embodiment of the present disclosure;

fig. 2 is a schematic diagram illustrating a training flow of each parameter model in an authentication shooting parameter optimization method according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a method for optimizing shooting parameters for authentication, applied to an actual scene, according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of a method for optimizing shooting parameters for authentication, which is applied to an actual scene according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an apparatus for optimizing authentication shooting parameters according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person skilled in the art without making creative efforts based on the embodiments in this document belong to the protection scope of this document.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

In order to solve the problem that in the prior art, when a user performs identity authentication, the user usually fails to perform the identity authentication because the user does not pass through the process of shooting identity documents and face images for many times, and further user experience is affected, embodiments of the present specification provide an optimization method for shooting parameters of the identity authentication. By adopting the method provided by the embodiment of the specification, when the user is authenticated, the face image of the target user and the certificate image of the target user can be obtained; performing identity verification on the target user based on the face image of the target user and the certificate image of the target user; if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during the identity authentication; and finally, inputting the behavior characteristic data and the first shooting parameters of the target user into a prediction model with a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data and the first shooting parameters of the target user so as to optimize the shooting parameters of the target user during identity verification based on the target shooting parameters.

The execution subject of the method for optimizing the shooting parameters of the identity authentication provided by the embodiment of the present disclosure may be, but is not limited to, a terminal device, a server, a personal computer, and the like, which can be configured to execute at least one of the terminals of the method provided by the embodiment of the present disclosure.

For convenience of description, the following description will be made of an embodiment of the method taking as an example that an execution subject of the method is a terminal device capable of executing the method. It is understood that the implementation of the method by the terminal device is only an exemplary illustration, and should not be construed as a limitation of the method.

Specifically, an implementation flow diagram of an identity verification photographing parameter optimization method provided in one or more embodiments of the present specification is shown in fig. 1, and includes:

s110, acquiring a face image of the target user and a certificate image of the target user.

It should be understood that, when the target user performs identity verification, it is usually necessary to verify the authenticity of the identity of the target user, i.e. to prove that the target user is not replaced by another person, and also to guarantee the legitimate rights and interests of the target user. The specific implementation manner of performing identity verification on the target user is generally to acquire a document (such as an identity document, a passport and other documents capable of proving the identity of the user) image uploaded by the target user and a collected face image of the target user, and further determine whether a face in the face image of the target user is consistent with a face in the document image of the target user.

And S120, performing identity verification on the target user based on the face image of the target user and the certificate image of the target user.

The identity of the target user is verified based on the face image of the target user and the certificate image of the target user, specifically, the face in the face image of the target user and the face in the certificate image of the target user can be obtained, and then whether the face in the face image of the target user is matched with the face in the certificate image of the target user (i.e., whether the face in the face image of the target user is consistent with the face in the certificate image of the target user or not) is determined, that is, whether the similarity is greater than or equal to a preset similarity threshold or not, for example, whether the similarity reaches more than. If the face in the face image of the target user is matched with the face in the certificate image of the target user, the identity of the target user can be verified; and if the face in the face image of the target user is not matched with the face in the certificate image of the target user, the identity verification of the target user can be determined to be failed.

S130, if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and first shooting parameters of the target user during the identity authentication.

It should be understood that, in order to avoid that the face image and the certificate image of the target user collected for multiple times are not matched, which causes multiple failures in the authentication of the target user, and further affects the use experience of the target user, a preset threshold may be set in the embodiment of this specification, and the preset threshold is used to indicate that after multiple failures occur in the authentication of the target user, the behavior feature data of the target user in a preset time period and the first shooting parameter of the target user during the authentication may be triggered to be acquired, and then the mode of optimizing the parameter of the target user during the authentication is entered. When the preset threshold is set to 1, the mode of optimizing the parameters of the target user during identity authentication can be entered as long as the target user fails in identity authentication.

Specifically, if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior feature data of the target user within a preset time period and a first shooting parameter of the target user during the identity authentication, including:

if the number of times of authentication failure of the target user is greater than or equal to a preset threshold value based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during authentication.

And S140, inputting the behavior characteristic data and the first shooting parameters of the target user into a prediction model with a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data and the first shooting parameters of the target user so as to optimize the shooting parameters of the target user during identity verification based on the target shooting parameters.

The human face quality threshold is used for judging whether the quality of the acquired human face is qualified; the certificate quality threshold is used for deciding whether the quality of the acquired certificate image is qualified; and the comparison threshold is used for deciding whether the face in the certificate image is consistent with the face in the face image.

Specifically, the prediction model of the certificate quality threshold is obtained by training with the certificate quality threshold in multiple identity verifications as a label and parameters except the certificate quality in multiple parameters as features; the prediction model of the face quality threshold is obtained by taking the face quality threshold during multiple identity verifications as a label and taking parameters except the face quality threshold in multiple parameters as features for training; the prediction model of the comparison threshold between the certificate picture and the face picture is obtained by taking the comparison threshold between the certificate picture and the face picture during multiple identity verifications as a label and taking parameters except the comparison threshold between the certificate picture and the face picture in multiple parameters as characteristic training.

Optionally, the predictive model of the plurality of parameters may further include at least one of:

the screen brightness prediction model is obtained by taking the screen brightness value during multiple identity verifications as a label and training by taking other parameters except the screen brightness in a plurality of parameters as characteristics;

the system comprises a prediction model of the case, wherein the prediction model of the case is obtained by taking the case in multiple times of identity verification as a label and taking other parameters except the case in multiple parameters as features for training;

the color pattern prediction model is obtained by training by taking the color pattern during multiple identity verification as a label and taking other parameters except the color pattern in multiple parameters as features;

the certificate counterfeiting threshold value prediction model is obtained by training by taking the certificate counterfeiting threshold value in multiple identity verification as a label and taking other parameters except the certificate counterfeiting threshold value in multiple parameters as features.

The screen brightness is the screen brightness of a terminal used by a target user for identity authentication; the file is specifically a file for guiding the target user to complete identity authentication; the color style is the color style of the authentication page when the target user performs authentication; the certificate forgery threshold is a threshold at which the target user can determine that the certificate image of the target user is forged when the target user performs authentication.

As shown in fig. 2, a schematic diagram of a training process of each parameter model in the method for optimizing the shooting parameters for authentication provided in an embodiment of the present disclosure is shown, in fig. 2, a prediction model of multiple parameters (fig. 2 is simply referred to as a parameter model) includes: a prediction model of screen brightness parameters (fig. 2 is simply referred to as a screen brightness model), a prediction model of a document (fig. 2 is simply referred to as a document model), a prediction model of document quality threshold values (fig. 2 is simply referred to as a document quality threshold value model), a prediction model of comparison threshold values (fig. 2 is simply referred to as a comparison threshold value model), a prediction model of face quality threshold values (fig. 2 is simply referred to as a face quality threshold value model), and other parameter models (including but not limited to a prediction model of color patterns and a prediction model of document forgery threshold values).

Taking the prediction model of the screen brightness parameter as an example, the training process of the prediction model of the screen brightness parameter may include: firstly, when identity authentication is carried out for multiple times in a historical time period, all listed parameters (including environmental parameters) and user behavior data are taken as characteristics, screen brightness of successful identity authentication in the historical time period is taken as a label, and other parameters are taken as characteristics; then, based on the multiple authentication, the screen brightness that is successfully authenticated is used as a label, and other parameters (all parameters except the screen brightness parameter) are used as features to train to obtain a prediction model (specifically, a regression model) of the screen brightness parameter.

The training process of the prediction model of other parameters is similar, and the embodiment of the present specification is not described herein again.

Optionally, in order to further improve the security level of the target user during identity authentication, in the embodiments of the present specification, when the shooting parameters of the target user during identity authentication are optimized, it may be determined whether a security risk exists based on the behavior feature data of the target user and the shooting parameters of the target user during identity authentication. Specifically, inputting the behavior characteristic data of the target user and a first shooting parameter of the target user during identity authentication into a prediction model of a plurality of parameters, and respectively outputting a target shooting parameter matched with the behavior characteristic data of the target user and the first shooting parameter, including:

determining whether a security risk exists in the target user during authentication, wherein the security risk comprises at least one of the security risk of the authentication device and the security risk of being attacked;

and if the target user does not have a safety risk during identity authentication, inputting the behavior characteristic data and the first shooting parameters of the target user into the prediction models of the multiple parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data and the first shooting parameters of the target user.

Optionally, inputting the behavior feature data of the target user and the first shooting parameter into a prediction model of a plurality of parameters, and outputting the target shooting parameter matched with the behavior feature data of the target user and the first shooting parameter, respectively, including:

extracting a first certificate quality threshold, a first face quality threshold, a first comparison threshold between the first certificate quality and the first face quality and environmental parameters when the target user performs identity verification from the behavior characteristic data and the first shooting parameters of the target user;

inputting a first face quality threshold, a first comparison threshold and environmental parameters into a prediction model of the certificate quality threshold, and outputting a target certificate quality threshold matched with the first face quality threshold and the first comparison threshold;

inputting a first face quality threshold, a first comparison threshold and environmental parameters into a prediction model of the face quality threshold, and outputting a target face quality threshold matched with the first face quality threshold and the first comparison threshold;

and inputting the first certificate quality threshold, the first face quality threshold and the environmental parameters into a prediction model of the comparison threshold, and outputting a target comparison threshold matched with the first certificate quality threshold and the first face quality threshold.

The environmental parameters include, but are not limited to, parameters such as ambient brightness, ambient pressure, ambient humidity, device temperature, and time when the target user performs authentication.

Optionally, in order to improve the passing rate of the identity authentication of the target user, after inputting the behavior feature data of the target user and the first shooting parameter into the prediction models of the multiple parameters, and outputting the target shooting parameter matched with the behavior feature data of the target user and the first shooting parameter, respectively, the method provided in the embodiment of the present specification further includes:

adjusting the shooting parameters of the target user during identity authentication according to the target shooting parameters;

after adjusting shooting parameters of a target user during identity authentication, acquiring a face image and a certificate image of the target user;

and performing identity verification on the target user based on the face image and the certificate image of the target user after the shooting parameters are adjusted.

Optionally, a parameter adjustment scheme may be generated by determining a difference between the current first shooting parameter value and the predicted preferred parameter value, and if the difference between the current first shooting parameter value and the predicted preferred parameter value exceeds a certain threshold, the current first shooting parameter value may be adjusted, otherwise, the adjustment may not be necessary. In particular, the preferred parameter value may be an optimal parameter value.

Specifically, adjusting the shooting parameters of the target user during identity authentication according to the target shooting parameters includes:

respectively determining a certificate quality difference value between a first certificate quality threshold value and a target certificate quality threshold value, a face quality difference value between a first face quality threshold value and a target human quality threshold value, and a comparison difference value between a first comparison threshold value and a target comparison threshold value based on the target shooting parameter and the first shooting parameter;

if the certificate quality difference is larger than or equal to the certificate quality threshold, adjusting the certificate quality threshold when the target user performs identity verification according to the target certificate quality threshold;

if the face quality difference is greater than or equal to the face quality threshold, adjusting the face quality threshold of the target user during identity authentication according to the target face quality threshold;

and if the comparison difference is larger than or equal to the comparison threshold, adjusting the comparison threshold when the target user performs identity verification according to the target comparison threshold.

As shown in fig. 3 and fig. 4, a schematic flow chart applied to an actual scene in the method for optimizing the shooting parameters for authentication provided in an embodiment of the present specification is shown.

Fig. 3 is a schematic diagram of a process of determining a face image and a certificate image based on a target user, and optimizing shooting parameters when performing authentication on the target user after the number of times of authentication failure of the target user is greater than or equal to a preset threshold, where the process includes:

s1, acquiring parameter data such as a certificate image, a face image, environmental parameters and the like shot during identity verification in a target user historical time period in real time, and recording the parameter data into a parameter library;

s2, when the failure times of the target user identity authentication exceed a preset threshold, triggering and starting an optimization parameter process, and reading recent behavior characteristic data of the target user;

s3, inputting the recent behavior characteristic data of the target user and the shooting parameters of the target user during identity verification into a parameter model group for prediction to obtain a group of optimized parameter adjustment schemes;

and S4, adjusting the shooting parameters of the target user during identity authentication according to the optimized parameter adjustment scheme, and guiding the target user to finish identity authentication again until the target user passes the identity authentication.

As shown in fig. 4, a schematic flow chart applied to an actual scene in the method for optimizing a set of complete authentication shooting parameters provided in the embodiment of the present specification includes:

s41, when the parameter optimizing process is started, judging whether the current environment has security risks, including the security of the equipment and the risk degree of user attack;

s42, if the current environment has no security risk, the parameter optimizing process is started, all the recent data are input into each parameter model in the parameter models, and the optimal value of each parameter in the current environment is predicted;

and S43, determining the parameter to be adjusted by judging the difference between the actual value of the parameter in the current environment and the predicted optimal value of the parameter. The ratio between the optimal value of the comparison parameter and the actual value of the parameter can be specifically adopted to determine which parameter is adjusted;

and S44, outputting the parameter adjusting scheme.

It should be understood that the preferred values of the parameters in step S42 under the current environment can be determined in many ways, and the embodiment of the present specification is not limited thereto. In particular, the preferred value of the parameter under the current circumstances may be the optimal value of the corresponding parameter.

Fig. 5 is a schematic structural diagram of an apparatus 500 for optimizing authentication shooting parameters, provided in one or more embodiments of the present specification, and includes:

an image acquisition module 510 for acquiring a face image of a target user and a certificate image of the target user;

an identity authentication module 520, configured to authenticate the target user based on the face image of the target user and the certificate image of the target user;

a parameter obtaining module 530, configured to, if authentication of the target user fails based on the face image and the certificate image of the target user, obtain behavior feature data of the target user within a preset time period and a first shooting parameter of the target user during authentication;

the parameter prediction module 540 is configured to input the behavior feature data of the target user and the first shooting parameter into a prediction model with multiple parameters, and output target shooting parameters matched with the behavior feature data of the target user and the first shooting parameter, respectively, so as to optimize the shooting parameters of the target user during identity authentication based on the target shooting parameters;

Optionally, in an embodiment, the parameter prediction module 540 is configured to:

determining whether a security risk exists in the target user during authentication, wherein the security risk comprises at least one of a security risk of an authentication device and an attacked security risk;

and if the target user does not have a safety risk during identity authentication, inputting the behavior characteristic data of the target user and the first shooting parameters into a prediction model of a plurality of parameters, and respectively outputting the target shooting parameters matched with the behavior characteristic data of the target user and the first shooting parameters.

extracting a first certificate quality threshold, a first face quality threshold, a first comparison threshold between the first certificate quality and the first face quality and environment parameters when the target user performs identity verification from the behavior characteristic data of the target user and the first shooting parameters;

inputting the first face quality threshold, the first comparison threshold and the environmental parameters into a prediction model of the certificate quality threshold, and outputting a target certificate quality threshold matched with the first face quality threshold and the first comparison threshold;

inputting the first face quality threshold, the first comparison threshold and the environmental parameters into a prediction model of the face quality threshold, and outputting a target face quality threshold matched with the first face quality threshold and the first comparison threshold;

Optionally, in an embodiment, the apparatus further includes:

the parameter adjusting module is used for adjusting the shooting parameters of the target user during identity authentication according to the target shooting parameters;

the image acquisition module is used for acquiring a face image and a certificate image of the target user after adjusting the shooting parameters of the target user during identity authentication;

and the identity verification module is used for verifying the identity of the target user based on the face image and the certificate image of the target user after the shooting parameters are adjusted.

Optionally, in an embodiment, the parameter adjusting module is configured to:

respectively determining a certificate quality difference value between the first certificate quality threshold and the target certificate quality threshold, a face quality difference value between the first face quality threshold and the target human quality threshold, and a comparison difference value between the first comparison threshold and the target comparison threshold based on the target shooting parameters and the first shooting parameters;

if the certificate quality difference is larger than or equal to a certificate quality threshold, adjusting the certificate quality threshold of the target user during identity verification according to the target certificate quality threshold;

if the human face quality difference is larger than or equal to a human face quality threshold, adjusting the human face quality threshold of the target user during identity verification according to the target human face quality threshold;

and if the comparison difference is larger than or equal to a comparison threshold, adjusting the comparison threshold when the target user performs identity verification according to the target comparison threshold.

Optionally, in an embodiment, the parameter obtaining module 530 is configured to:

and if the number of times of the authentication failure of the target user is greater than or equal to a preset threshold value based on the face image and the certificate image of the target user, acquiring behavior characteristic data of the target user in a preset time period and a first shooting parameter of the target user during the authentication.

Optionally, in an embodiment, the prediction model of the plurality of parameters further includes at least one of:

the prediction model of the screen brightness is obtained by training by taking the screen brightness value during multiple times of identity verification as a label and taking other parameters except the screen brightness in the multiple parameters as features;

the system comprises a prediction model of the case, wherein the prediction model of the case is obtained by training by taking the case in multiple times of identity verification as a label and taking other parameters except the case in the multiple parameters as features;

the color pattern prediction model is obtained by training by taking the color patterns during multiple identity verifications as labels and taking other parameters except the color patterns in the multiple parameters as features;

the certificate counterfeiting threshold value prediction model is obtained by training by taking the certificate counterfeiting threshold value in multiple identity verifications as a label and taking other parameters except the certificate counterfeiting threshold value in the multiple parameters as features.

The apparatus 500 for optimizing authentication shooting parameters can implement the methods in the embodiments of the methods shown in fig. 1 to fig. 4, and specifically refer to the method for optimizing authentication shooting parameters in the embodiment shown in fig. 1, which is not described again.

Fig. 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the optimization device of the shooting parameters of the identity authentication on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

The method for optimizing the shooting parameters for identity verification disclosed in the embodiment of fig. 1 in the present specification can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also 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. The various methods, steps and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

The electronic device may further execute the method in fig. 1, and implement the functions of the apparatus for optimizing shooting parameters based on authentication in the embodiment shown in fig. 1, which are not described herein again in this specification.

Of course, besides the software implementation, the electronic device in this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

Embodiments of the present specification also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, are capable of causing the portable electronic device to perform the method of the embodiment shown in fig. 1, and in particular to perform the following:

In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present disclosure should be included in the scope of protection of one or more embodiments of the present disclosure.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims

1. An identity authentication shooting parameter optimization method comprises the following steps:

2. The method of claim 1, wherein the inputting the behavior feature data of the target user and the first photographing parameter into a prediction model of a plurality of parameters, and outputting the target photographing parameter matching the behavior feature data of the target user and the first photographing parameter, respectively, comprises:

3. The method of any one of claims 1 or 2, wherein the step of inputting the behavior feature data of the target user and the first shooting parameters into a prediction model of a plurality of parameters and outputting the target shooting parameters matched with the behavior feature data of the target user and the first shooting parameters respectively comprises the steps of:

4. The method of claim 3, after inputting the behavior feature data of the target user and the first photographing parameter into a prediction model of a plurality of parameters and outputting target photographing parameters matching the behavior feature data of the target user and the first photographing parameter, respectively, the method further comprising:

after the shooting parameters of the target user during identity authentication are adjusted, acquiring a face image and a certificate image of the target user;

5. The method of claim 4, wherein adjusting the shooting parameters for the target user to perform authentication according to the target shooting parameters comprises:

6. The method of claim 1, wherein if the identity authentication of the target user fails based on the face image and the certificate image of the target user, acquiring behavior feature data of the target user within a preset time period and first shooting parameters of the target user during identity authentication comprises:

7. The method of claim 1, the predictive model of the plurality of parameters further comprising at least one of:

8. An apparatus for optimizing photographing parameters for authentication, comprising:

9. An electronic device, comprising:

a processor; and

10. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to: