WO2020037962A1

WO2020037962A1 - Facial image correction method and apparatus, and storage medium

Info

Publication number: WO2020037962A1
Application number: PCT/CN2019/075929
Authority: WO
Inventors: 陈日伟
Original assignee: 北京字节跳动网络技术有限公司
Priority date: 2018-08-24
Filing date: 2019-02-22
Publication date: 2020-02-27
Also published as: CN109241890B; CN109241890A

Abstract

Provided is a method for correcting a facial image, comprising: establishing multiple sample libraries of species, wherein a facial image of a sample ID species of a corresponding species is stored in each sample library; respectively learning samples in a multi-type sample library by using a machine learning algorithm with regard to a plurality of facial images of the species with the same sample ID to obtain a multi-classification recognition model with regard to different species; acquiring a plurality of facial images of an object to be recognized; respectively parsing the plurality of facial images of the object to be recognized by using the multi-classification recognition model to obtain a plurality of facial features; and performing corrective recognition on the plurality of facial features on the facial images of the object to be recognized by using a modification model. According to the present disclosure, fast simultaneous collection of dynamic and static facial images with regard to a plurality of organisms can be realized, and the technical effects of accurate facial recognition and adding special effects to faces of the organisms can also be realized. The present disclosure also relates to a facial image correction apparatus.

Description

Face image correction method, device and storage medium

This application claims priority from a Chinese patent application filed on August 24, 2018 with the Chinese Patent Office, application number 201810975874.7, and application name "Face Image Correction Method, Device, and Storage Medium", the entire contents of which are incorporated herein by reference. Applying.

Technical field

The present disclosure relates to the field of artificial intelligence technology, and in particular, to a method, a device, and a storage medium for facial image correction.

Background technique

The description of the background technology of the present disclosure belongs to the related technology related to the present disclosure, and is only used to explain and facilitate the understanding of the inventive content of the present disclosure. Prior art at the filing date.

With the rapid development of science and technology, more and more electronic multimedia technologies have been applied to people's daily lives, and people have more and more ways of entertainment and leisure. Among them, music creative short video social software that shoots short videos is one of them. This method can add special effects to the face of a person when shooting or editing a short video to increase the entertainment effect. However, the current short video social software cannot collect special objects with non-fixed poses on the camera, such as the faces of animals and babies, to increase special effects, so it cannot meet the needs of users.

Summary of the Invention

A first aspect of the present disclosure relates to a method for correcting a facial image, which specifically includes the steps of establishing a multi-type sample bank to establish a multi-species sample bank, wherein the sample ID of each species stores the sample ID of the corresponding species and the face of the species Image; multi-class recognition model training step, using a machine learning algorithm to learn samples in multiple types of sample libraries for multiple said face images of a species with the same sample ID to obtain a multi-class recognition model for different species; obtaining multiple faces An image step to obtain multiple face images of the object to be identified; a multi-class recognition model to parse multiple face images; to use the multi-class recognition model to parse multiple face images of the object to be identified to obtain multiple facial features; and to correct the recognition step, A plurality of facial features on the facial image of the object to be recognized are corrected and identified by using a correction model.

The disclosure can realize the simultaneous acquisition of dynamic and static facial images for multiple creatures at the same time, especially for specific objects with unfixed postures, such as animals and babies. Even if a positive posture avatar cannot be collected, accurate facial recognition can be achieved. Add special effects to the face.

According to the present disclosure, the optional embodiment further includes a special effect rendering step, which uses special effect tools to perform special effect rendering on the corrected and recognized face image.

According to the present disclosure, the step of parsing a plurality of facial images by a multi-class recognition model in an optional embodiment includes: using a deep convolutional neural network to parse a plurality of the facial images of an object to be identified, respectively, to obtain a plurality of facial features.

According to the present disclosure, an optional embodiment further includes a pre-processing step of pre-processing a plurality of the facial images of the acquired object to be identified.

A second aspect of the present disclosure relates to a facial image correction device, including: a multi-type sample library establishing module to establish a multi-species sample library, wherein the sample library of each species stores a sample ID of a corresponding species and a face image of the species ; Classification recognition model training module, for a plurality of said facial images of a species with the same sample ID, respectively, using a machine learning algorithm to learn samples from a multi-type sample library to obtain a multi-class recognition model for different species; obtaining multiple facial images The module obtains multiple facial images of the object to be identified; the multiple classification recognition model parses multiple facial image modules; uses the multiple classification recognition model to parse multiple facial images of the object to be identified separately to obtain multiple facial features; corrects the identification module, uses corrections The model corrects and recognizes multiple facial features on the facial image of the object to be recognized.

According to the present disclosure, in an optional implementation manner, the device further includes a special effect rendering module for performing special effect rendering on the face image corrected and identified by using a special effect tool.

According to the present disclosure, in an optional implementation manner, the multi-class recognition model in the device parses multiple facial image modules, and is configured to use a deep convolutional neural network to parse multiple multiple facial images of an object to be identified respectively. Multiple facial features.

According to an optional embodiment of the present disclosure, the device further includes a pre-processing module that pre-processes a plurality of the facial images of the acquired object to be identified.

A third aspect of the present disclosure relates to a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements any of the foregoing when the program is executed. Steps of face image correction method.

A fourth aspect of the present disclosure relates to a computer-readable storage medium having stored thereon a computer program, wherein when the program is executed by a processor, the steps of any one of the facial image correction methods described above are implemented.

Additional aspects and advantages of the disclosure will become apparent in the following description, or be learned through practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

1 is a schematic flowchart of a first embodiment of a facial image correction method according to the present disclosure;

2 is a block diagram of a first embodiment of a facial image correction apparatus according to the present disclosure;

3 is a schematic flowchart of a second embodiment of a facial image correction method according to the present disclosure;

4 is a block diagram of a second embodiment of a facial image correction apparatus according to the present disclosure;

5 is a schematic flowchart of a third embodiment of a facial image correction method according to the present disclosure;

6 is a block diagram of a third embodiment of a facial image correction apparatus according to the present disclosure;

7 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present disclosure;

8 is a schematic diagram of a hardware structure of a human-computer interaction device according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure.

detailed description

In order to more clearly understand the foregoing objectives, features, and advantages of the present disclosure, the present disclosure is described in further detail below with reference to the accompanying drawings and specific embodiments. Although each embodiment represents a single combination of inventions, different embodiments of the present disclosure may be substituted or combined, so the disclosure may also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment contains A, B, C and another embodiment contains a combination of B and D, then this disclosure should also be considered to include one or more of all other possible combinations containing A, B, C, D Embodiment, although this embodiment may not be clearly written in the following content.

In the following description, many specific details are set forth in order to fully understand the present disclosure. However, the present disclosure can also be implemented in other ways than those described herein. Therefore, the scope of protection of the present disclosure is not limited by the specifics disclosed below. Limitations of Examples.

Example 1

As shown in FIG. 1, a first aspect of the present disclosure relates to a facial image correction method, including:

In step 101, a multi-type sample bank establishing step is performed to establish a multi-species sample bank, in which a sample ID of each species and a face image of the species are stored in the sample bank of each species. For example, the face sample database LFPW, AFLW, BioID, ICCV13, MVFW, and olivettifaces; the cat face sample database can randomly collect a sufficient number (such as 200) of cat breeds of various breeds as the cat face sample database; the dog face sample database can Randomly collect a sufficient number (such as 200) of dog faces of various breeds as a dog face sample library.

In step 102, a multi-class recognition model training step is performed on multiple face images of a species with the same sample ID by using a machine learning algorithm to learn samples in a multi-type sample library to obtain multi-class recognition models for different species.

According to an exemplary embodiment, during the learning process, machine algorithm learning is performed on the same learning sample in groups to obtain a recognition model for the species. For example, the multi-class recognition model includes forming a face recognition group for a human face, a cat face recognition group for a cat, and a dog face recognition group for a dog. It can also go further and form a European face recognition group for Europeans and a Persian cat face recognition group for Persian cats.

In step 103, a plurality of facial image acquisition steps are performed to acquire a plurality of facial images of the object to be identified.

Obtain multiple facial pictures of the object to be identified. The picture format is not limited. The specific method of acquisition may be real-time acquisition from a camera or input from an image library. It should be noted that, here, for example, there may be multiple facial pictures taken at regular intervals (such as every 1 s), and the object to be identified may also be multiple creatures at the same time, for example, two cats, or one person and one dog. .

In step 104, the multi-class recognition model parses multiple facial images, and uses the multi-class recognition model to parse multiple facial images of the object to be recognized separately to obtain multiple facial features.

The multi-classifier can be one or more of various facial recognition detection algorithms, such as geometric feature-based algorithms, local feature algorithms, eigenface algorithms, elastic model-based algorithms, and neural network algorithms. For example, a facial sample image matching the facial image of the to-be-recognized object is searched in the face image sample library according to the feature vector of the facial image of the to-be-recognized object, and the facial features of the facial image of the to-be-recognized object are determined according to the facial sample image. . By calculating the vector distance between the feature vector of the face image of the object to be recognized and the feature vector of the face sample image, the face sample image with the smallest or smaller vector distance is used as the face sample image that matches the face image of the object to be recognized. The facial features of the facial sample image are the facial features of the facial image of the object to be identified.

In the above manner, a plurality of facial images of an object to be recognized may be separately analyzed by using a deep convolutional neural network to obtain a plurality of facial features.

Step 105: a correction recognition step, using a correction model to perform correction recognition on a plurality of facial features on a facial image of a subject to be recognized.

On the basis of the previous step, the beneficial effect of continuing the correction and recognition step of the present disclosure is that a good recognition result can be obtained even if an image that closely matches the multi-type sample library is not obtained. A specific implementation manner may be, for example, calculating a rotation amount of an object to be identified according to a functional relationship between a depth value of the facial feature and the features, and fitting a front image of the object to be identified based on obtaining multiple facial images of the object to be identified. To match the multi-type sample library again to obtain recognition results.

It should be noted that, as described above, if the object to be identified includes multiple organisms, the identification of multiple organisms is performed simultaneously.

In addition, it should also be noted that the facial image correction method of the present disclosure further includes: step 106, a special effect rendering step, using a special effect tool to perform special effect rendering on the face image that is identified and corrected. Use various functional rendering tools to perform various special effects rendering on the identified facial images, such as stickers (with garlands, glasses, hair coloring, beauty, dressing), deformation, stretching, liquefaction, and so on.

The present disclosure can achieve the simultaneous acquisition of dynamic and static facial images for multiple creatures at the same time, especially for specific objects with fixed postures, such as animals and babies. Since they do not cooperate with facial image acquisition, it is difficult to capture a still positive facial image. The present disclosure realizes that even if a facial image of a positive posture cannot be collected, that is, it can be understood as a person's avatar in a narrow sense, accurate facial image recognition can be realized, and a special technical effect is added to its face.

A second aspect of the embodiment of the present disclosure relates to a face image correction apparatus. The face image correction apparatus of the embodiment of the present invention is described below with reference to FIG. 2. First of all, it should be noted that the foregoing explanation of the method embodiment is also applicable to the device of this embodiment, and details are not described herein again.

A multi-type sample bank building module 201 establishes a multi-species sample bank, in which a sample ID of a corresponding species and a face image of the species are stored in the sample bank of each species.

The classification recognition model training module 202 uses machine learning algorithms to respectively learn samples from multiple types of sample libraries for multiple facial images of species with the same sample ID to obtain multi-class recognition models for different species.

The multiple facial image module 203 acquires multiple facial images of an object to be identified.

The multi-class recognition model analyzes multiple face image modules 204, and uses the multi-class recognition model to parse multiple face images of the object to be recognized separately to obtain multi-face features.

The correction recognition module 205 performs correction recognition on a plurality of facial features on a facial image of a subject to be identified by using a correction model.

In addition, it should be noted that the facial image correction device according to the present disclosure further includes: a special effect rendering module 206 for performing special effect rendering on the face image corrected and identified by using a special effect tool.

Example two

In image recognition, the quality of the image directly affects the design of the recognition algorithm and the accuracy of the effect. Therefore, in addition to being able to optimize the algorithm, the preprocessing technology occupies a very important factor in the entire project.

This embodiment takes into account factors such as light and darkness, shadows, and complex backgrounds during the real-time image acquisition process. Therefore, in an optional embodiment, the obtained image to be identified may be pre-processed first to eliminate irrelevant images in the image. Information, recover useful real information, enhance the detectability of the information and simplify the data to the greatest extent, thereby improving the reliability of feature extraction, image segmentation, matching and recognition.

As shown in FIG. 3, in step 103 ', the obtained image to be identified is pre-processed first. The pre-processing process generally includes steps such as digitization, geometric transformation, normalization, smoothing, restoration, and enhancement, which are not repeated here.

The facial image correction device of this embodiment is described below with reference to FIG. 4. First of all, it should be noted that the foregoing explanation of the method embodiment is also applicable to the device of this embodiment, and details are not described herein again.

As shown in FIG. 4, the facial image correction device further includes a pre-processing module 203 ′, which pre-processes the obtained image to be identified first.

Example three

This embodiment is an optional embodiment, specifically adopting various parameters required to obtain the correction function from the calculation of using multiple facial features, and then establishing a mapping function through the mapping relationship between the facial feature points of the multiple images, for multi-angle facial images Correction is performed to adjust the errors caused by non-frontal image recognition. Referring to FIG. 5, step 105 may specifically include:

In step 1051, a parameter acquisition step uses multiple facial features to calculate and obtain parameters required for the correction function.

For example, the image depth of the above-mentioned characteristic points of the mouth, nose tip, eyes, and eyebrows in each face image is calculated separately from each facial feature in the obtained multiple facial images; and in each face image, the mouth, nose tip, eyes, The number of pixels of the above-mentioned feature points of the eyebrows, the left or right rotation angle of the face is calculated based on the mutual position relationship of the feature points, and the three parameters of the image depth, the number of pixels, and the rotation angle are corrected to finally obtain a frontal facial image.

In step 1052, a function correction step completes a correction operation for a multi-angle facial image by using the parameters in the obtained correction function.

It should be noted that the three parameters of the acquired image depth, the number of pixels, and the rotation angle and the parameter ranges of the image depth, the number of pixels, and the rotation angle of the front face image learned in advance are adjusted and corrected. At the same time, Get the correction function. It should be noted that the correction function includes a vector group of facial feature points of the corrected facial image, a vector group of facial feature points obtained by obtaining facial images of different species, and a relationship with the corrected facial image in the correction function. The matching of the facial feature points completes the operation of correcting and identifying multiple facial features in a three-dimensional reconstruction state on the facial image of the object to be recognized by modifying the model. In addition, it can be understood that in the face correction step, considering that different face regions have different importance for three-dimensional face recognition, the present disclosure adopts a neuron position-sensitive matching mode. The main purpose of the neuron position-sensitive matching pattern is to obtain the deep convolution features at different neuron positions through a training database as the weights for 3D facial image recognition. In the neuron position-sensitive matching mode, in the recognition process, the weight is combined with the traditional sparse representation classifier, that is, based on the neuron position-sensitive matching mode, the sparse representation model is used to calculate multiple facial features. The parameters required for the correction function are obtained to achieve a three-dimensional face comparison.

The facial image correction device of this embodiment is described below with reference to FIG. 6. First of all, it should be noted that the foregoing explanation of the method embodiment is also applicable to the device of this embodiment, and details are not described herein again.

As shown in FIG. 6, the correction recognition module 205 of the facial image correction device specifically includes:

The parameter obtaining unit 2051 calculates parameters required for the correction function by using multiple facial features, such as a rotation angle, an image depth, and a number of pixels.

The function correction unit 2052 completes a correction operation for a multi-angle face image by using the parameters in the obtained correction function.

In addition, as shown in FIG. 7, the face image correction method and apparatus of the present disclosure can be implemented on a terminal device. The terminal device may be implemented in various forms, and the terminal device in the present disclosure may include, but is not limited to, such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (Portable multimedia player), a navigation device, a vehicle-mounted terminal device, a vehicle-mounted display terminal, a vehicle-mounted electronic rear-view mirror, and the like, and a mobile terminal device such as a digital TV, a desktop computer, and the like.

In one embodiment of the present disclosure, the terminal device may include a wireless communication unit 1, an A / V (audio / video) input unit 2, a user input unit 3, a sensing unit 4, an output unit 5, a memory 6, and an interface unit 7. , Controller 8 and power supply unit 9 and so on. The A / V (audio / video) input unit 2 includes, but is not limited to, a camera, a front camera, a rear camera, and various audio and video input devices. Those skilled in the art should understand that the components included in the terminal device listed in the foregoing embodiments are not limited to the types described above, and may include fewer or more components.

Those skilled in the art will understand that the various embodiments described herein may be implemented in a computer-readable medium using, for example, computer software, hardware, or any combination thereof. For hardware implementation, the implementations described herein can be implemented by using application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays ( (FPGA), processor, controller, microcontroller, microprocessor, electronic unit designed to perform the functions described herein, and in some cases, such an implementation may be in the controller Implementation. For software implementation, an implementation such as a process or function may be implemented with a separate software module allowing at least one function or operation to be performed. The software code can be implemented by a software application (or program) written in any suitable programming language, and the software code can be stored in a memory and executed by a controller.

A facial image correction device 80 provided by an embodiment of the third aspect of the present disclosure includes a memory 801, a processor 802, and a program stored on the memory and executable on the processor. When the processor executes the program, any one of the foregoing is specific. Steps of a method for adding special effects to a subject's face.

In one embodiment of the present disclosure, the memory is used to store non-transitory computer-readable instructions. According to example embodiments, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like. In one embodiment of the present disclosure, the processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the human-machine interaction device to execute Expected function. In an embodiment of the present disclosure, the processor is configured to execute computer-readable instructions stored in the memory, so that the facial image correction apparatus executes the foregoing facial image correction method.

In one embodiment of the present disclosure, as shown in FIG. 8, the face image correction device 80 includes a memory 801 and a processor 802. The components in the face image correction device 80 are interconnected by a bus system and / or other forms of connection mechanisms (not shown).

The memory 801 is configured to store non-transitory computer-readable instructions. According to example embodiments, the memory 801 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, and the like.

The processor 802 may be a central processing unit (CPU) or other form of processing unit having data processing capabilities and / or instruction execution capabilities, and may control other components in the face image correction apparatus 80 to perform a desired function. In an embodiment of the present disclosure, the processor 802 is configured to execute computer-readable instructions stored in the memory 801, so that the facial image correction apparatus 80 executes the method for adding special effects to the face of a specific object. The face image correction device is the same as the embodiment described above for the method of adding special effects to the face of a specific object, and the repeated description will be omitted here.

As shown in FIG. 9, a computer-readable storage medium 900 provided by an embodiment of the fourth aspect of the present disclosure stores a computer program thereon. When the program is executed by a processor, any one of the above-mentioned methods for adding special effects to the face of a specific object is stored. Method steps. The computer-readable storage medium may include, but is not limited to, any type of disk, including flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), static random access memory (SRAM), and electrically erasable memory. In addition to programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, floppy disks, optical disks, DVDs, CD-ROMs, micro-drives and magneto-optical disks, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, Flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data. In one embodiment of the present disclosure, the computer-readable storage medium 900 has non-transitory computer-readable instructions 901 stored thereon. When the non-transitory computer-readable instructions 901 are executed by the processor, a method for adding special effects to the face of a specific subject according to the embodiment of the present disclosure described with reference to the above is performed.

In the present disclosure, the terms "installation", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, "connected" may be a fixed connection, a detachable connection, or an integral connection. ; "Connected" can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific situations.

In the description of this specification, the descriptions of the terms “one embodiment”, “some embodiments”, “specific embodiments” and the like mean that specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in the present disclosure In at least one embodiment or example. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

The above description is only an optional embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modification, equivalent replacement, or improvement made within the spirit and principle of this disclosure shall be included in the protection scope of this disclosure.

Claims

A facial image correction method includes:

The multi-type sample bank establishment step establishes a multi-species sample bank, in which the sample ID of each species stores the sample ID of the corresponding species and the face image of the species;

A multi-class recognition model training step, in which multiple face images of a species with the same sample ID are used to learn samples in a multi-type sample library by using a machine learning algorithm to obtain a multi-class recognition model for different species;

Steps of obtaining multiple facial images to obtain multiple facial images of an object to be identified;

The multi-class recognition model parses multiple facial image steps, and uses the multi-class recognition model to parse multiple facial images of the object to be recognized separately to obtain multiple facial features;

The correction recognition step performs correction recognition on a plurality of facial features on a facial image of a subject to be identified using a correction model.
The method according to claim 1, further comprising:

The special effect rendering step uses special effects tools to perform special effect rendering on the corrected face image.
The method for facial image correction according to claim 1, wherein the step of parsing a plurality of facial images by the multi-class recognition model comprises: using a deep convolutional neural network to parse a plurality of the facial images of an object to be identified to obtain a plurality of Facial features.
The method for correcting a facial image according to claim 1, further comprising:

The pre-processing step is to pre-process multiple acquired facial images of the object to be identified.
The method for correcting a facial image according to claim 1, wherein the correction recognition step of the method comprises:

A parameter acquisition step, which uses multiple facial features to calculate parameters required for the correction function;

The function correction step completes a correction operation for the face image at multiple angles by using the parameters in the correction function obtained.
A facial image correction device includes:

A multi-type sample library building module to establish a multi-species sample library, in which the sample ID of each species and the face image of the species are stored in the sample library of each species;

A classification recognition model training module, which uses a machine learning algorithm to learn samples from multiple types of sample libraries for multiple said facial images of a species with the same sample ID to obtain a multi-class recognition model for different species;

Acquiring multiple facial image modules to acquire multiple facial images of an object to be identified;

The multi-class recognition model parses multiple facial image modules, and uses the multi-class recognition model to parse multiple facial images of the object to be recognized separately to obtain multi-face features;

The correction recognition module performs correction recognition on a plurality of facial features on a facial image of an object to be identified using a correction model.
The facial image correction device according to claim 6, further comprising:

The special effect rendering module is used to perform special effect rendering on the face image corrected and recognized by using the special effect tool.
The facial image correction device according to claim 6, wherein the multi-class recognition model in the device analyzes a plurality of facial image modules for respectively using a deep convolutional neural network to a plurality of the facial images of an object to be identified. Analyze to get multiple facial features.
The facial image correction apparatus according to claim 6, further comprising:

The pre-processing module pre-processes the plurality of facial images of the acquired object to be identified.
The facial image correction device according to claim 6, wherein the correction recognition module further comprises:

A parameter acquisition module for calculating parameters required for the correction function by using multiple facial features;

A function correction module is configured to complete a correction operation for the facial image at multiple angles by using the obtained parameters in the correction function.
A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements any of the claims 1-5 when the processor executes the program. The steps of the method described.
A computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the steps of the method according to any one of claims 1-5.