WO2022127480A1

WO2022127480A1 - Facial recognition method and related device

Info

Publication number: WO2022127480A1
Application number: PCT/CN2021/131045
Authority: WO
Inventors: 李亚英; 孟春芝; 蔡进; 李潇婧; 王琼瑶; 吴倩
Original assignee: 展讯通信(天津)有限公司
Priority date: 2020-12-15
Filing date: 2021-11-17
Publication date: 2022-06-23
Also published as: CN112651311A

Abstract

The present invention relates to the technical field of image recognition, and particularly relates to a facial recognition method and a related device. The method comprises: identifying a facial area image from an image to be subjected to detection; according to a real facial pixel feature, a false facial pixel feature and a non-facial pixel feature, classifying pixel points included in the facial area image into real facial pixels, false facial pixels and non-facial pixels; and determining, according to the proportion of the number of real facial pixels in the facial area image, whether said image is a living body facial image. By means of the method, the type of each pixel point in a facial area image can be determined, thereby avoiding the influence of non-facial pixels on facial recognition, and improving the accuracy of facial recognition.

Description

A face recognition method and related equipment

This application claims the priority of the Chinese patent application filed on December 15, 2020 with the application number 202011478613.8 and the invention titled "A face recognition method and related equipment", the entire contents of which are incorporated herein by reference Applying.

technical field

The invention relates to the field of image recognition, in particular to a face recognition method and related equipment.

Background technique

With the gradual maturity of face recognition technology, face recognition technology has been applied to more fields. For example, face payment, access control recognition, and remote transactions. In the above application fields, there are some loopholes, for example, criminals can use other people's photos, videos and 3D masks to bypass face recognition. For this problem, a common solution in the prior art is to adopt an interactive verification method. Specifically, the user makes a specified action according to the prompt, and when the user completes the specified action, the verification is deemed to be passed. However, such methods require the user to perform multiple sets of actions, the recognition process is long, the user experience is poor, and the system is prone to misjudgment when the user interacts. Therefore, how to accurately and quickly recognize the face of a living body is a problem to be solved at present.

Application content

In order to solve the above problems, embodiments of the present invention provide a face recognition method and system, which can improve the accuracy of living face recognition.

In a first aspect, an embodiment of the present invention provides a face recognition method, including:

Identify the face region image from the image to be detected;

According to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, the pixels included in the face area image are divided into real face pixels, fake face pixels and non-face pixels;

Whether the image to be detected is a living face image is determined according to the proportion of the number of real face pixels in the face area image.

In the above scheme, the image of the face area is identified from the image to be detected, and each pixel in the image of the face area is divided into real face pixels, fake face pixels and non-face pixels, and according to the number of real face pixels. The proportion occupied in the image of the face area determines whether the image to be detected is a living face image. As a result, the accuracy of live face recognition can be improved.

In one possible implementation manner, the pixels included in the face region image are divided into real face pixels, fake face pixels and non-face pixels according to the real face pixel features, the fake face pixel features and the non-face pixel features pixels, including:

Inputting the face region image into the first recognition model;

The first recognition model determines, according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, each pixel included in the face region image as the real face pixel, the fake face pixel and the non-face pixel respectively. .

In one possible implementation manner, the first recognition model obtained by training includes:

Determine a training image set and a test image set, where the training image set includes: a living face image and a non-living face image; each pixel of the living face image and the non-living face image is marked with a pixel point respectively type, the pixel type includes real face pixels, fake face pixels and non-face pixels; the test image set includes test face images;

The first recognition model is trained by using the features of real face pixels, fake face pixels and non-face pixels included in each image of the training image set, so that the first recognition model learns the features of real face pixels, fake face pixels and fake face pixels. Features and non-face pixel features;

Using the test face images included in the test image set, a pixel classification test is performed on the first recognition model, so that the first recognition model classifies each pixel in the test face image.

In one possible implementation manner, determining whether the to-be-detected image is a living face image is determined according to the proportion of the number of real face pixels in the face region image, including:

According to the ratio of the real face pixels to the fake face pixels and/or the number of non-face pixels, it is determined whether the to-be-detected image is a living face image.

In one of the possible implementations, determining whether the image to be detected is a living face image is determined according to the ratio of the real face pixels to the fake face pixels and/or the number of the non-face pixels, including:

Whether the image to be detected is a living face image is determined according to the ratio of the number of real face pixels to the sum of the first pixels, wherein the sum of the first pixels is the difference between the real face pixels and the fake face pixels sum of numbers.

In one possible implementation manner, determining whether the image to be detected is a living face image is determined according to the ratio of the number of real face pixels to the sum of the first pixels, including:

If the ratio of the number of real face pixels to the sum of the first pixels is greater than or equal to the first threshold, then determine that the image to be detected is a living face image;

Otherwise, it is determined that the to-be-detected image is a non-living face image.

In a second aspect, an embodiment of the present invention provides a face recognition device, including:

The recognition module is used to recognize the face area image from the image to be detected;

a pixel classification module, used for dividing the pixels contained in the face region image into real face pixels, fake face pixels and non-face pixels according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature;

The determining module determines whether the to-be-detected image is a living human face image according to the proportion of the number of real face pixels in the face region image.

In one of the possible implementations, the pixel classification module is configured to classify the pixels contained in the face region image into real face pixels according to real face pixel features, fake face pixel features and non-face pixel features , fake face pixels, and non-face pixels, including:

Inputting the face region image into the first recognition model;

In one possible implementation manner, the face recognition device includes:

at least one processor; and

at least one memory communicatively coupled to the processor, wherein:

The memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method as provided in the first aspect of the claim.

In a third aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method provided in the first aspect.

It should be understood that the second to third aspects of the embodiments of the present invention are consistent with the technical solutions of the first aspect of the embodiments of the present invention, and the beneficial effects obtained by various aspects and corresponding feasible implementations are similar, and will not be repeated.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings that need to be used in the embodiments. Obviously, the drawings in the following description are only some examples of the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

1 is a flowchart of a face recognition method provided by an embodiment of the present invention;

2 is a flowchart of another face recognition method provided by an embodiment of the present invention;

3 is a schematic structural diagram of a face recognition device according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

In order to better understand the technical solutions of the present specification, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

It should be understood that the described embodiments are only a part of the embodiments of the present specification, but not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present specification. As used in the embodiments of the present invention and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

In the related art, when the face is recognized, it will be affected by the non-face factor, which makes the detection result deviate. When judging whether the face to be detected is a living body, the process is complicated and it is easy to misjudge. In order to solve the above problems, in the embodiment of the present invention, by identifying the face area in the image to be detected, each pixel in the face area is divided into three categories: real face pixels, fake face pixels, and non-face pixels, and according to the real face pixels The ratio between face pixels and face area pixels determines whether the image to be detected is a living body.

FIG. 1 is a flowchart of a face recognition method provided by an embodiment of the present invention. As shown in FIG. 1 , the above-mentioned face recognition method may include:

Step 101: Identify the face region image from the image to be detected. The image to be detected may be an image captured by a camera of the detection terminal. From the images captured by the camera, one or several frames of images are selected as the images to be detected. The detection terminal may be a mobile phone, an automatic teller machine of a bank, etc. The detection terminal takes an image of the object to be detected and saves it by setting a camera. For the saved image of the object to be detected, the face part in the image can be identified, and an image of the face region corresponding to the image to be detected can be obtained. The image of the face area may be an image of a rectangular frame area including a face in the image to be detected.

Step 102: According to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, the pixels included in the face region image are divided into real face pixels, fake face pixels and non-face pixels. Generally, the image of the face region identified from the image to be detected is a rectangle, while the face is actually an ellipse. Therefore, the face area image will contain some non-face parts, such as the background environment, and the corresponding pixels on the face area image are non-face pixels. For a live face, the corresponding pixels on the face region image are real face pixels. For a non-living face, such as a video or photo face, the corresponding pixels on the face area image are fake face pixels. In this embodiment of the present invention, each pixel in the face region image can be classified according to the real face pixel feature, the fake face pixel feature, and the non-face pixel feature, and the classification of each pixel in the face region image is obtained to obtain the classification status of each pixel in the face region image. Avoid the interference of non-face parts or fake face parts to face recognition.

Step 103: Determine whether the to-be-detected image is a living human face image according to the proportion of the number of real face pixels in the face region image. For example, when the proportion of the number of real face pixels in the face area image is greater than or equal to 70%, it can be determined that the image to be detected is a living face image. If the proportion of real face pixels in the face area image is less than 70%, it can be determined that the image to be detected is not a living face image.

In some embodiments, each pixel in the face region can be classified by the recognition model. The steps of the method include: inputting the face region image into a first recognition model; the first recognition model, according to the real face pixel feature, the false face pixel feature and the non-face pixel feature, The pixels are determined as real face pixels, fake face pixels and non-face pixels respectively. Wherein, the first recognition model may be, for example, a recognition model obtained by deep learning based on a convolutional neural network.

In some embodiments, the training method of the first recognition model is shown in FIG. 2 , and the method steps include:

Step 201: Determine a training image set and a test image set. The training image set includes: living face images and non-living face images. Each pixel of the living face image and the non-living face image is respectively marked with a pixel type, and the pixel type includes real face pixels, fake face pixels and non-face pixels. The set of test images includes test face images.

Step 202, utilize the features of real face pixels, fake face pixels and non-face pixels included in each image of the training image set to train the first recognition model, so that the first recognition model learns the real face pixel features, Fake face pixel features and non-face pixel features.

Step 203, using the test face image included in the test image set to perform a pixel classification test on the first recognition model, so that the first recognition model classifies each pixel in the test face image . Through the above training method, the first recognition model can have the ability to recognize real face pixels, fake face pixels and non-face pixels. The first recognition model can classify each pixel in the image to be detected.

In some embodiments, after the real face pixels, fake face pixels and non-face pixels in the face region image of the image to be detected are determined, the non-face pixels can be eliminated as interference items, and only the real face pixels and the The relationship between the fake face pixels is used to determine whether the image to be detected is a living human face. The method includes: determining the ratio of the number of real face pixels to the sum of the first pixels; if the ratio is greater than or equal to the first threshold, Then, it is determined that the image to be detected is a living face image; otherwise, it is determined that the image to be detected is a non-living face image. The sum of the first pixels may be the sum of the number of fake face pixels, the sum of the number of non-human face pixels, or the sum of the numbers of real face pixels and fake face pixels.

For example, the face region image of the image to be detected includes 1000 real face pixels, 100 fake face pixels, and 200 non-face pixels, and the first threshold may be 0.8. When the sum of the first pixel values is the sum of the real face pixels and the fake face pixels, the sum of the first pixels is 1100, and the ratio of the real face pixels to the sum of the first pixels is 1000/1100. At this time, the ratio of the real face pixel to the sum of the first pixel is greater than the first threshold, and it can be determined that the image to be detected is a living face image.

By classifying each pixel in the face area image, the interference of non-face factors can be excluded, and the accuracy of live face recognition can be improved.

Corresponding to the above face recognition method, an embodiment of the present invention provides a face recognition system. As shown in FIG. 3 , the face recognition system may include: a recognition module 301 , a pixel classification module 302 and a determination module 303 . in,

The identification module 301 is used to identify the image of the face region from the image to be detected.

The pixel classification module 302 is configured to divide the pixels included in the face region image into real face pixels, fake face pixels and non-face pixels according to the real face pixel features, the fake face pixel features and the non-face pixel features.

The determining module 303 is configured to determine whether the to-be-detected image is a living human face image according to the proportion of the number of real face pixels in the face region image.

In some embodiments, the pixel classification module 302 is specifically configured to: input the face region image into a first recognition model; the first recognition model is based on real face pixel features, fake face pixel features and non-face pixels feature, and each pixel included in the face region image is determined as a real face pixel, a fake face pixel and a non-face pixel, respectively.

The face recognition system provided by the embodiment shown in FIG. 3 can be used to implement the technical solutions of the method embodiments shown in FIG. 1 to FIG. 2 of this specification, and the implementation principle and technical effect can further refer to the relevant descriptions in the method embodiments.

FIG. 4 is a schematic structural diagram of an embodiment of an electronic device in this specification. As shown in FIG. 4 , the electronic device may include at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores data that can be processed The above-mentioned processor invokes the above-mentioned program instructions to execute the face recognition method provided by the embodiment shown in FIG. 1 of this specification.

Figure 4 shows a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present specification. The electronic device shown in FIG. 4 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present specification.

As shown in Figure 4, the electronic device takes the form of a general-purpose computing device. Components of an electronic device may include, but are not limited to, one or more processors 410 , a communication interface 420 , a memory 430 , a communication bus 440 connecting various system components including memory 430 , communication interface 420 and processing unit 410 .

Communication bus 440 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (hereinafter referred to as: ISA) bus, Micro Channel Architecture (hereinafter referred to as: MAC) bus, enhanced ISA bus, video electronics Standards Association (Video Electronics Standards Association; hereinafter referred to as: VESA) local bus and Peripheral Component Interconnection (Peripheral Component Interconnection; hereinafter referred to as: PCI) bus.

Electronic devices typically include various computer system readable media. These media can be any available media that can be accessed by the electronic device, including both volatile and nonvolatile media, removable and non-removable media.

The memory 430 may include a computer system readable medium in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter referred to as: RAM) and/or cache memory. The electronic device may further include other removable/non-removable, volatile/non-volatile computer system storage media. Memory 330 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present invention.

A program/utility having a set (at least one) of program modules that may be stored in memory 430, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data , each or some combination of these examples may include an implementation of a network environment. Program modules typically perform the functions and/or methods of the embodiments described in this specification.

The processor 410 executes various functional applications and data processing by running the programs stored in the memory 430 , for example, implementing the face recognition method provided by the embodiments shown in FIGS. 1 to 2 of this specification.

Embodiments of this specification provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the embodiments shown in FIG. 1 to FIG. 2 of this specification Provided face recognition method.

The above-described non-transitory computer-readable storage media may employ any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of computer readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (Read Only Memory) ; hereinafter referred to as: ROM), erasable programmable read only memory (Erasable Programmable Read Only Memory; hereinafter referred to as: EPROM) or flash memory, optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic memory components, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .

Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out the operations of this specification may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or Wide Area Network (WAN), or it can Connect to an external computer (eg via the Internet using an Internet Service Provider).

The foregoing describes specific embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of this specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present specification, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined.

Any process or method description in the flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing custom logical functions or steps of the process , and the scope of the preferred embodiments of this specification includes alternative implementations in which the functions may be performed out of the order shown or discussed, including performing the functions substantially concurrently or in the reverse order depending upon the functions involved, which should It is understood by those skilled in the art to which the embodiments of this specification belong.

Depending on the context, the word "if" as used herein can be interpreted as "at" or "when" or "in response to determining" or "in response to detecting." Similarly, the phrases "if determined" or "if detected (the stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (the stated condition or event)," depending on the context )" or "in response to detection (a stated condition or event)".

It should be noted that the terminals involved in the embodiments of this specification may include but are not limited to personal computers (Personal Computer; hereinafter referred to as: PC), personal digital assistants (Personal Digital Assistant; hereinafter referred to as: PDA), wireless handheld devices, tablet Computer (Tablet Computer), mobile phone, MP3 player, MP4 player, etc.

In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined. Or it can be integrated into another system, or some features can be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

In addition, each functional unit in each embodiment of this specification may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional units.

The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (Processor) to execute the methods described in the various embodiments of this specification. some steps. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (Read-Only Memory; hereinafter referred to as: ROM), Random Access Memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk and other various A medium on which program code can be stored.

The above descriptions are only preferred embodiments of this specification, and are not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification shall be included in this specification. within the scope of protection.

Claims

A face recognition method, comprising:

Identify the face region image from the image to be detected;

According to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, the pixels included in the face area image are divided into real face pixels, fake face pixels and non-face pixels;

Whether the image to be detected is a living face image is determined according to the proportion of the number of real face pixels in the face area image.
The method according to claim 1, wherein, according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, the pixels included in the face area image are divided into real face pixels and fake face pixels. and non-face pixels, including:

Inputting the face region image into the first recognition model;

The first recognition model determines, according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, each pixel included in the face region image as the real face pixel, the fake face pixel and the non-face pixel respectively. .
The method according to claim 2, wherein obtaining the first recognition model by training comprises:

Determine a training image set and a test image set, where the training image set includes: a living face image and a non-living face image; each pixel of the living face image and the non-living face image is marked with a pixel point respectively type, the pixel type includes real face pixels, fake face pixels and non-face pixels; the test image set includes test face images;

The first recognition model is trained by using the features of real face pixels, fake face pixels and non-face pixels included in each image of the training image set, so that the first recognition model learns the features of real face pixels, fake face pixels and fake face pixels. Features and non-face pixel features;

Using the test face images included in the test image set, a pixel classification test is performed on the first recognition model, so that the first recognition model classifies each pixel in the test face image.
The method according to claim 1, wherein determining whether the to-be-detected image is a living human face image is determined according to the proportion of the number of real face pixels in the face region image, comprising:

According to the ratio of the real face pixels to the fake face pixels and/or the number of non-face pixels, it is determined whether the to-be-detected image is a living face image.
The method according to claim 4, wherein whether the image to be detected is a living human face is determined according to the ratio of the real face pixels to the number of fake face pixels and/or the number of non-face pixels images, including:

Determine whether the image to be detected is a living face image according to the ratio of the number of real face pixels to the sum of the first pixels, where the sum of the first pixels is the real face pixels and the fake face The sum of the number of pixels.
The method according to claim 5, wherein determining whether the to-be-detected image is a living face image is determined according to the ratio of the number of real face pixels to the sum of the first pixels, comprising:

If the ratio of the number of real face pixels to the sum of the first pixels is greater than or equal to the first threshold, then determine that the image to be detected is a living face image;

Otherwise, it is determined that the to-be-detected image is a non-living face image.
A face recognition device, comprising:

The recognition module is used to recognize the face area image from the image to be detected;

a pixel classification module, used for dividing the pixels contained in the face region image into real face pixels, fake face pixels and non-face pixels according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature;

A determination module, configured to determine whether the to-be-detected image is a living face image according to the proportion of the number of real face pixels in the image of the face region.
The device according to claim 7, wherein the pixel classification module is specifically configured to input the face region image into the first recognition model;

The first recognition model determines, according to the real face pixel feature, the fake face pixel feature and the non-face pixel feature, each pixel included in the face region image as the real face pixel, the fake face pixel and the non-face pixel respectively. .
A face recognition device, comprising:

at least one processor; and

at least one memory communicatively coupled to the processor, wherein:

The memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method as claimed in any one of claims 1 to 6.
A non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to perform the execution of any one of claims 1 to 6. Methods.