CN110909189A - Method and device for processing face picture - Google Patents
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- CN110909189A CN110909189A CN201911220552.2A CN201911220552A CN110909189A CN 110909189 A CN110909189 A CN 110909189A CN 201911220552 A CN201911220552 A CN 201911220552A CN 110909189 A CN110909189 A CN 110909189A
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- 230000004048 modification Effects 0.000 claims description 48
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- 230000006870 function Effects 0.000 claims description 20
- 230000002596 correlated effect Effects 0.000 claims description 5
- 238000012512 characterization method Methods 0.000 claims description 4
- 238000011478 gradient descent method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 238000004891 communication Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/51—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
Abstract
A method and device for processing human face pictures are disclosed. Based on the thought of the countermeasure sample, the face picture of the user in the business side face library is modified, and the modified face picture needs to have the following effects: the user can be identified by the service party and the user can not be identified by at least one non-service party. The face picture of the user in the business side face library can be replaced by the modified face picture meeting the effect, so that the privacy of the user is protected.
Description
Technical Field
The embodiment of the specification relates to the technical field of information, in particular to a method and a device for processing a face picture.
Background
At present, a business party increasingly adopts a face recognition technology to authenticate a user. Specifically, the business party needs to store the face picture of the user in the face library in advance, so that when the user needs to perform identity authentication, the business party can acquire the face picture of the user and compare the acquired face picture with the face picture in the face library to complete the identity authentication.
However, the face image is important privacy of the user, and once the face image in the face library is disclosed, the result of harm which is difficult to measure is often caused to the user.
Disclosure of Invention
In order to reduce the possibility of misusing a face picture of a user after leakage, embodiments of the present specification provide a method and an apparatus for processing a face picture, and a technical scheme is as follows:
according to the 1 st aspect of the embodiments of the present specification, there is provided a method for processing a face picture, including:
acquiring a face picture of a target user from a business side face library;
modifying the face picture based on a preset picture modification model, and judging whether the modified face picture meets specified conditions;
if so, replacing the face picture in the business side face library with the modified face picture;
if not, adjusting parameters of the image modification model, and modifying the face image again based on the adjusted image modification model;
if the modified face picture meets the specified condition, the representation business party can identify the target user according to the modified face picture, and at least one non-business party cannot identify the target user according to the modified face picture.
According to the 2 nd aspect of the embodiments of the present specification, there is provided an apparatus for processing a face picture, including:
the acquisition module is used for acquiring a face picture of a target user from a business side face library;
the modification judgment module modifies the face picture based on a preset picture modification model and judges whether the modified face picture meets specified conditions or not;
if yes, replacing the face picture in the business side face library with the modified face picture;
the second processing module is used for adjusting the parameters of the image modification model if the human face image is not modified, and modifying the human face image again based on the adjusted image modification model;
if the modified face picture meets the specified condition, the representation business party can identify the target user according to the modified face picture, and at least one non-business party cannot identify the target user according to the modified face picture.
The technical solution provided in the embodiment of the present specification modifies the face picture of the user in the business side face library based on the thought of the confrontation sample, and the modified face picture needs to have the following effects: the user can be identified by the service party and the user can not be identified by at least one non-service party. The face picture of the user in the business side face library can be replaced by the modified face picture which meets the effect.
According to the embodiment of the specification, the face pictures in the face library of the business party are obtained by modifying the original face pictures of the user, and the face pictures usually have no use value for non-business parties, so that the face pictures in the face library of the business party are not easily abused even if the face pictures are leaked out.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of embodiments of the invention.
In addition, any one of the embodiments in the present specification is not required to achieve all of the effects described above.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present specification, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flowchart of a method for processing a face picture according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an apparatus for processing a face picture according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an apparatus for configuring a method according to an embodiment of the present disclosure.
Detailed Description
Once the face pictures in the face library of the business party are leaked to the non-business party, the face pictures are easily abused, and harmful consequences are caused to users. For example, a non-business party may use a picture of a user's face to unlock the user's cell phone, or may use a picture of the user's face to steal the user's account funds. However, in practice, on one hand, the service party needs to perform user identity authentication on the service based on the face library of the service party, and on the other hand, the service party is difficult to completely prevent the face pictures in the face library of the service party from being leaked.
When the applicant thinks that the image can be modified for each user face image in the face library of the business party when the applicant thinks of the technical scheme for solving the technical problems, the business party can still recognize the user according to the modified face image, but the non-business party cannot recognize the user according to the modified face image. Even if the face picture of a certain user in the face library of the business party is leaked to the non-business party, the non-business party is difficult to utilize the face picture to cause harmful consequences.
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present specification, the technical solutions in the embodiments of the present specification will be described in detail below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of protection.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a method for processing a face picture according to an embodiment of the present disclosure, including the following steps:
s100: and acquiring a face picture of the target user from the face library of the service party.
In the embodiment of the present specification, a service party refers to a subject that obtains a face picture of a user through user agreement, so as to perform identity authentication related to a service on the user. The business party may specifically be an internet service provider.
The non-business party refers to a main body which is not approved by the user and has no authority to use the face picture of the user. The non-business party may be an individual or an organization.
For convenience of description, the processing method of the face picture of the target user is taken as an example for explanation. The target user can be any user served by the service party, and can also be a specific user served by the service party.
S102: and modifying the face picture based on a picture modification model.
S104: and judging whether the modified face picture meets specified conditions, if so, executing step S106, and otherwise, executing step S108.
In the embodiment of the present specification, the picture modification model may refer to a picture modification rule, or may be a model constructed based on a machine learning algorithm.
It should be noted here that the image modification model may be iteratively adjusted until the face image modified based on the image modification model satisfies a specified condition. When the picture modified model is initialized in advance, the picture modified model can be set randomly or artificially.
In an embodiment of the present specification, if the modified face picture meets the specified condition, a representation service party can identify the target user according to the modified face picture, and at least one non-service party cannot identify the target user according to the modified face picture.
It should be noted here that there may be a plurality of non-business parties, and in the embodiment of the present specification, the modified face picture may only enable one non-business party to be unable to identify the target user according to the modified face picture, so that the probability of misuse after the face picture is leaked can also be reduced (i.e., if the face picture is leaked to the non-business party, it is difficult to be utilized). In addition, the modified face picture can also prevent a plurality of or all non-business parties from recognizing the target user according to the modified face picture.
The business party typically uses the face picture based on a business party face recognition model. In practical applications, if the modified face picture is required not to be recognized by human eyes as the target user, the specified condition may be set as: and the business side face recognition model recognizes the target user according to the modified face picture, and the difference representation value is greater than a specified value. Wherein the larger the difference characterization value is, the larger the difference between the face picture and the modified face picture is.
If the modified face image is required not to be recognized by at least one other face recognition model except the business side face recognition model to identify the identity of the target user, the specified condition may be set as: the target user can be identified by the business side face recognition model according to the modified face picture, and the target user can not be identified by at least one non-business side face recognition model according to the modified face picture.
S106: and replacing the face picture in the face library of the business party with the modified face picture.
If the modified face picture meets the specified conditions, the face picture in the business side face library can be replaced by the modified face picture, and the method flow is ended.
S108: and adjusting parameters of the picture modification model, and returning to execute the step S102.
If the modified face picture does not meet the specified conditions, the parameters of the picture modification model can be adjusted, and the method returns to step S102 to continue to execute the method flow.
It should be noted here that, alternatively, the picture modification model is a plurality of picture modification rules, and the parameter may be a parameter of each picture modification rule.
In this manner, the parameters of the picture modification model may be randomly adjusted when step S108 is executed. With one iteration, one successful adjustment always occurs, so that the face picture modified by the adjusted picture modification model meets the specified condition.
Alternatively, the picture modification model is a model constructed based on a machine learning algorithm, and the parameter may be a model parameter. For example, if the picture modification model is constructed based on a convolutional neural network algorithm, the above parameters may be a convolutional kernel matrix in a convolutional neural network convolutional layer and a weight value in a fully-connected layer.
In another manner, the parameters of the picture modification model can be purposefully modified when step S108 is executed.
Specifically, the specified condition may be that the value of the objective function (or referred to as a loss function) is smaller than a specified threshold, or that the number of times of adjusting the picture modification model is larger than a specified number of times.
Wherein the value of the objective function and P0Is negatively correlated with PiPositive correlation; p0Identifying the confidence probability, P, of the target user for the business side face recognition model according to the modified face pictureiAnd for the ith non-business side face recognition model, recognizing the confidence probability of the target user according to the modified face picture, wherein i is (1,2, … N), and N is the number of the non-business side face recognition models.
It should be noted that the face recognition model recognizes the confidence probability of the target user according to the modified face image, and represents the confidence degree of the recognition result that the modified face image output by the face recognition model belongs to the target user, and the higher the confidence probability is, the higher the confidence degree is.
An objective function is given here by way of example, and it should be understood that this does not constitute a limitation on the embodiments of the present description. Assuming that the number N of non-business side face recognition models is 3, the objective function L may be:
L＝1-P0+P1+P2+P3。
of course, the specified conditions may be set to: the value of the objective function is greater than a specified threshold, in which case the value of the objective function is equal to P0Is positively correlated with PiA negative correlation. The objective function L maySo that:
L＝P0+1-P1+1-P2+1-P3
in this way, the parameters of the image modification model can be modified by adopting a gradient descent method, so that the modified face image meeting the specified conditions can be obtained through fewer iterations as far as possible.
By the method shown in fig. 1, the face picture of the user in the business side face library is modified, and the modified face picture needs to have the following effects: the user can be identified by the service party and the user can not be identified by at least one non-service party. The face picture of the user in the business side face library can be replaced by the modified face picture which meets the effect.
Because the face pictures in the face library of the business party are obtained by modifying the original face pictures of the user, the face pictures have no use value for the non-business party, and therefore, the face pictures in the face library of the business party are not easy to be abused even if the face pictures are leaked out.
Fig. 2 is a schematic structural diagram of an apparatus for processing a face picture according to an embodiment of the present disclosure, including:
an obtaining module 201, which obtains a face picture of a target user from a business side face library;
the modification judgment module 202 modifies the face picture based on a preset picture modification model, and judges whether the modified face picture meets a specified condition;
if yes, the first processing module 203 replaces the face picture in the business side face library with the modified face picture;
if not, the second processing module 204 adjusts parameters of the image modification model, and modifies the face image again based on the adjusted image modification model;
if the modified face picture meets the specified condition, the representation business party can identify the target user according to the modified face picture, and at least one non-business party cannot identify the target user according to the modified face picture.
The specified conditions include:
the business side face recognition model recognizes the target user according to the modified face picture, and the difference representation value is larger than a specified value; the larger the difference characterization value is, the larger the difference between the face picture and the modified face picture is.
The specified conditions include:
the value of the target function is smaller than a specified threshold value, or the number of times of adjusting the picture modification model is larger than a specified number of times;
wherein the value of the objective function and P0Is negatively correlated with PiPositive correlation; p0Identifying the confidence probability, P, of the target user for the business side face recognition model according to the modified face pictureiAnd for the ith non-business side face recognition model, recognizing the confidence probability of the target user according to the modified face picture, wherein i is (1,2, … N), and N is the number of the non-business side face recognition models.
The second processing module 204 adjusts parameters of the picture modification model by a gradient descent method according to the value of the objective function.
The second processing module 204 randomly adjusts parameters of the picture modification model.
Embodiments of the present specification also provide a computer device, which at least includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method shown in fig. 1 when executing the program.
Fig. 3 is a schematic diagram illustrating a more specific hardware structure of a computing device according to an embodiment of the present disclosure, where the computing device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Embodiments of the present description also provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the method shown in fig. 1.
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.
From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a service device, or a network device) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The systems, methods, modules or units described in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
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 apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, and the modules described as separate components may or may not be physically separate, and the functions of the modules may be implemented in one or more software and/or hardware when implementing the embodiments of the present disclosure. And part or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The foregoing is only a specific embodiment of the embodiments of the present disclosure, and it should be noted that, for those skilled in the art, a plurality of modifications and decorations can be made without departing from the principle of the embodiments of the present disclosure, and these modifications and decorations should also be regarded as the protection scope of the embodiments of the present disclosure.
Claims (11)
1. A method of processing a picture of a human face, comprising:
acquiring a face picture of a target user from a business side face library;
modifying the face picture based on a preset picture modification model, and judging whether the modified face picture meets specified conditions;
if so, replacing the face picture in the business side face library with the modified face picture;
if not, adjusting parameters of the image modification model, and modifying the face image again based on the adjusted image modification model;
if the modified face picture meets the specified condition, the representation business party can identify the target user according to the modified face picture, and at least one non-business party cannot identify the target user according to the modified face picture.
2. The method of claim 1, the specified conditions comprising:
the business side face recognition model recognizes the target user according to the modified face picture, and the difference representation value is larger than a specified value; the larger the difference characterization value is, the larger the difference between the face picture and the modified face picture is.
3. The method of claim 1, the specified conditions comprising:
the value of the target function is smaller than a specified threshold value, or the number of times of adjusting the picture modification model is larger than a specified number of times;
wherein the value of the objective function and P0Is negatively correlated with PiPositive correlation; p0Identifying the confidence probability, P, of the target user for the business side face recognition model according to the modified face pictureiAnd for the ith non-business side face recognition model, recognizing the confidence probability of the target user according to the modified face picture, wherein i is (1,2, … N), and N is the number of the non-business side face recognition models.
4. The method according to claim 3, wherein adjusting the parameters of the picture modification model specifically comprises:
and adjusting parameters of the picture modification model by adopting a gradient descent method according to the value of the target function.
5. The method according to claim 1, wherein adjusting the parameters of the picture modification model specifically comprises:
and randomly adjusting parameters of the picture modification model.
6. An apparatus for processing a picture of a human face, comprising:
the acquisition module is used for acquiring a face picture of a target user from a business side face library;
the modification judgment module modifies the face picture based on a preset picture modification model and judges whether the modified face picture meets specified conditions or not;
if yes, replacing the face picture in the business side face library with the modified face picture;
the second processing module is used for adjusting the parameters of the image modification model if the human face image is not modified, and modifying the human face image again based on the adjusted image modification model;
if the modified face picture meets the specified condition, the representation business party can identify the target user according to the modified face picture, and at least one non-business party cannot identify the target user according to the modified face picture.
7. The apparatus of claim 6, the specified conditions comprising:
the business side face recognition model recognizes the target user according to the modified face picture, and the difference representation value is larger than a specified value; the larger the difference characterization value is, the larger the difference between the face picture and the modified face picture is.
8. The apparatus of claim 6, the specified conditions comprising:
the value of the target function is smaller than a specified threshold value, or the number of times of adjusting the picture modification model is larger than a specified number of times;
wherein the value of the objective function and P0Is negatively correlated with PiPositive correlation; p0Identifying the confidence probability, P, of the target user for the business side face recognition model according to the modified face pictureiAnd for the ith non-business side face recognition model, recognizing the confidence probability of the target user according to the modified face picture, wherein i is (1,2, … N), and N is the number of the non-business side face recognition models.
9. The apparatus of claim 8, wherein the second processing module adjusts parameters of the picture modification model using a gradient descent method according to the value of the objective function.
10. The apparatus of claim 6, said second processing module to randomly adjust parameters of said picture modification model.
11. 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 program implements the method of any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111160357A (en) * | 2020-04-02 | 2020-05-15 | 支付宝(杭州)信息技术有限公司 | Model training and picture output method and device based on counterstudy |
| CN111739168A (en) * | 2020-06-30 | 2020-10-02 | 华东交通大学 | Large-scale three-dimensional face synthesis method with suppressed sample similarity |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020106114A1 (en) * | 2000-12-01 | 2002-08-08 | Jie Yan | System and method for face recognition using synthesized training images |
| CN108520184A (en) * | 2018-04-16 | 2018-09-11 | 成都博锐智晟科技有限公司 | A kind of method and system of secret protection |
| CN109359210A (en) * | 2018-08-09 | 2019-02-19 | 中国科学院信息工程研究所 | The face retrieval method and system of double blind secret protection |
| CN109918881A (en) * | 2019-01-22 | 2019-06-21 | 西安电子科技大学 | The online face authentication method and system of secret protection, intelligent movable equipment |
-
2019
- 2019-12-03 CN CN201911220552.2A patent/CN110909189A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020106114A1 (en) * | 2000-12-01 | 2002-08-08 | Jie Yan | System and method for face recognition using synthesized training images |
| CN108520184A (en) * | 2018-04-16 | 2018-09-11 | 成都博锐智晟科技有限公司 | A kind of method and system of secret protection |
| CN109359210A (en) * | 2018-08-09 | 2019-02-19 | 中国科学院信息工程研究所 | The face retrieval method and system of double blind secret protection |
| CN109918881A (en) * | 2019-01-22 | 2019-06-21 | 西安电子科技大学 | The online face authentication method and system of secret protection, intelligent movable equipment |
Non-Patent Citations (1)
| Title |
|---|
| 章坚武等: "卷积神经网络的人脸隐私保护识别", 《中国图象图形学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111160357A (en) * | 2020-04-02 | 2020-05-15 | 支付宝(杭州)信息技术有限公司 | Model training and picture output method and device based on counterstudy |
| CN111160357B (en) * | 2020-04-02 | 2020-06-30 | 支付宝(杭州)信息技术有限公司 | Model training and picture output method and device based on counterstudy |
| CN111739168A (en) * | 2020-06-30 | 2020-10-02 | 华东交通大学 | Large-scale three-dimensional face synthesis method with suppressed sample similarity |
| CN111739168B (en) * | 2020-06-30 | 2021-01-29 | 华东交通大学 | Large-scale three-dimensional face synthesis method with suppressed sample similarity |
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