WO2020087922A1

WO2020087922A1 - Facial attribute identification method, device, computer device and storage medium

Info

Publication number: WO2020087922A1
Application number: PCT/CN2019/089662
Authority: WO
Inventors: 张国辉; 赵玉玲
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-10-30
Filing date: 2019-05-31
Publication date: 2020-05-07
Also published as: CN109522824A

Abstract

A facial attribute identification method, an identification device, a computer device and a storage medium, wherein the method comprises: constructing a parallel computation model, comprising a main node and multiple task nodes connected to the main node (S11); acquiring a target image by means of the main node, allocating the target image to a storage space corresponding to a first category of task nodes, and triggering the first category of task nodes to identify whether the target image comprises a facial image (S12); when the target image comprises a facial image, extracting the facial image by means of the first category of task nodes, and returning the facial image to the main node (S13); by means of the main node, allocating the facial image to a storage space corresponding to each of a second category of task nodes, and triggering all of the second category of task nodes to perform parallel computation on all of the facial attributes of the facial image (S14); returning a computation result of the facial attributes of each of the second category of task nodes to the main node, and triggering the main node to summarize the computation results of all of the facial attributes (S15); and outputting the summarized result of the main node (S16). In the method, different facial attributes of a facial image are computed by means of a parallel computation model to improve computational efficiency and help the subsequent searching of a face, thereby effectively screening a target population.

Description

Face attribute recognition method, device, computer device and storage medium

This application requires the priority of the Chinese patent application submitted to the China Patent Office on October 30, 2018, with the application number 201811281100.0 and the invention titled "face recognition method, device, computer device and storage medium". The reference is incorporated in this application.

Technical field

The present application relates to face attribute recognition, and in particular to a face attribute recognition method, a face attribute recognition device, a computer device, and a storage medium.

Background technique

In daily life, the need for identification widely exists in all walks of life, such as financial services, customs entry and exit, national security and other fields, all need to identify the identity of people. With the development of technology, the advantages of biometrics in the field of identity recognition are becoming more and more obvious. Among them, face recognition is a research direction that has developed rapidly in recent years. Face recognition collects user images through a camera. When a face is detected, the user image is identified by attributes (such as gender, age, posture, or expression, etc.), and then the extracted attributes are compared with the preset attribute information stored in the database. Yes, and further realize the user's face recognition.

However, the recognition of different attributes in the prior art is usually based on mutually independent algorithms, which results in only one attribute being able to recognize a face at a time, and the recognition efficiency is low.

Summary of the invention

In view of the above, it is necessary to propose a face attribute recognition method, a face attribute recognition device, a computer device, and a storage medium that can improve recognition efficiency, so as to solve the above problems.

A preferred embodiment of the present application provides a face attribute recognition method, including: constructing a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, for each task Nodes allocate corresponding storage space, where all the task nodes are divided into a first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute; through the master node Acquire a target image, and allocate the target image to the storage space corresponding to the first type of task node, trigger the first type of task node to calculate the target image to identify whether the target image Including a face image; when the obtained target image includes a face image, extract the face image from the target image through the first type of task node, and return the extracted image to the master node Face image; assign the face image to the storage space corresponding to each task node of the second type through the master node to trigger all the The second-type task node performs parallel calculation on all face attributes of the stored face images to obtain the calculation result of all face attributes; and returns the calculation of the face attributes of each second-type task node to the master node As a result, the master node is triggered to summarize the calculation results of all face attributes.

A preferred embodiment of the present application further provides a face attribute recognition device, including: a building module for building a parallel computing model, the parallel computing model includes a master node and a plurality of tasks connected to the master node Node, allocates corresponding storage space for each task node, wherein all the task nodes are divided into a first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute ; The distribution module is used to obtain a target image through the master node, and allocate the target image to the storage space corresponding to the first type of task node, triggering the first type of task node to the target image Performing calculations to identify whether the target image includes a face image;

An extraction module for extracting the face image from the target image through the first-type task node when the acquired target image includes a face image; and a return module for returning to the master node The face image extracted by the extraction module; the allocation module is also used to allocate the face image to the storage space corresponding to each task node of the second type through the master node to trigger all the The second type of task node performs parallel calculation on all face attributes of the stored face images to obtain the calculation result of all face attributes; and the return module is also used to return each second type to the master node The calculation result of the face attributes of the task node triggers the master node to summarize the calculation results of the face attributes of all people.

A preferred embodiment of the present application further provides a computer device, including a processor and a memory, at least one computer-readable instruction is stored in the memory, and the processor is used to execute the computer-readable instruction to implement the foregoing The face attribute recognition method mentioned above.

A preferred embodiment of the present application further provides a non-volatile readable storage medium. The non-volatile readable storage medium stores at least one computer-readable instruction. When the computer-readable instruction is executed by a processor Implement the face attribute recognition method as described above.

This application uses the parallel computing model to calculate different face attributes of the face image in parallel, which is conducive to improving computing efficiency; the subsequent face attribute recognition method can be imported into various public occasions, such as stations, subways, shopping malls, Supermarkets, by acquiring the target images collected by the cameras on these occasions, and then recognizing various face attributes, and comparing the recognition results with the recognition results of the face attributes of specific groups of people (such as criminals), based on matching Screen the target population efficiently.

BRIEF DESCRIPTION

FIG. 1 is a flowchart of a face attribute recognition method provided in Embodiment 1 of the present application.

2 is a schematic diagram of geometric normalization of a face image in the face attribute recognition method shown in FIG. 1.

FIG. 3 is a schematic structural diagram of a face attribute recognition apparatus provided in Embodiment 2 of the present application.

4 is a schematic diagram of a computer device provided in Embodiment 3 of the present application.

detailed description

FIG. 1 is a flowchart of a face attribute recognition method provided by a preferred embodiment of the present application. The face attribute recognition method is applied to a computer device 1. According to different requirements, the order of steps of the face attribute recognition method may be changed, and some steps may be omitted or combined. The face attribute recognition method includes the following steps:

Step S11: Construct a parallel computing model. The parallel computing model includes a master node and a plurality of task nodes connected to the master node, and a corresponding storage space is allocated to each task node, where all the task nodes are It is divided into a first type task node and at least two second type task nodes, and each second type task node corresponds to a face attribute.

Among them, the parallel computing model may use a random access parallel machine (Parallel Random Access Machine, PRAM) model, an overall synchronous parallel computing model (Bulk Synchronous Parallel Computing Model, BSP), a LogP model, and a block distributed storage (Block Distributed Model, BDM) One of the models. The main node of the parallel computing model is the input point of the target image to be recognized and the task assignment node TaskTrack. The task node is an operation point of face attributes, that is, a task execution node JobTrack. The first type of task node is used to calculate and identify whether the target image includes a face image. The second type of task node is used to calculate and recognize the face attribute of the face image when the target image includes a face image.

Wherein, the face attributes include gender, age, ethnicity, expression, etc. The number of task nodes of the second type can be set according to the number of face attributes to be calculated, and the more face attributes to be calculated, the more the number of task nodes of the second type. For example, the parallel computing model may include four task nodes of the second type, and the face attributes corresponding to the four task nodes of the second type are gender, age, race, and expression, respectively.

Step S12: Obtain a target image through the master node, and allocate the target image to the storage space corresponding to the first-type task node to trigger the first-type task node to calculate the target image, To identify whether a face image is included in the target image.

Wherein, the user can send the processing request of the target image stored in the designated directory of the terminal device to the master node through a terminal device (such as a mobile phone, a tablet computer, etc.). When receiving the processing request, the master node obtains the target image through the designated directory.

In this embodiment, before identifying whether the target image includes a face image, the method further includes: triggering the first type of task node to perform preprocessing on the target image to exclude interference from environmental factors such as light.

Due to the influence of light, there are many uncertainties in the target image during the shooting process, such as light intensity, light source direction, color, etc., so that the gray scale of the target image is uneven, and the face has a large local contrast. As a result, the final recognition effect is affected, so it is necessary to use light adjustment technology to adjust the light to the acquired target image.

Further, the recognizing whether the target image includes a face image includes:

a) Trigger the first type of task node to scan whether the target image contains information of preset local features of a human face;

b) When the image contains information on preset local features of a human face, it is determined that the target image includes a human face image.

Specifically, the preset local feature may be a nose, eyes, mouth, etc. When scanning the target image includes the preset local feature, it is determined that the target image includes a face image.

Step S13: When the acquired target image includes a face image, extract the face image from the target image through the first-type task node to realize living body detection and recognition, and return the extracted to the master node The face image.

In this embodiment, the extracting the face image from the target image through the first type of task node includes:

a) judging the number of face images included in the target image by the first type of task node;

b) When the number of face images included in the target image is only one, the first type of task node delimits a rectangular boundary around the face image, and extracts the human face according to the rectangular boundary image;

c) When the number of face images included in the target image is at least two, the rectangular boundaries around each face image are delimited by the first-type task nodes, and the people defined by each rectangular boundary are calculated The area of the face image, and select one of the largest rectangular boundaries to extract the face image;

d) Normalize the geometric characteristics of the extracted face image through the first type of task node, the normalization of the geometric characteristics can normalize the face image to the same position, Angle and size. Since the distance between two eyes of a person is basically the same for most people, the position of the two eyes is usually used as the basis for geometric normalization of the face image.

Specifically, as shown in FIG. 2, assuming that the positions of the two eyes in the face image are El and Er, respectively, the geometric normalization of the face image can be achieved through the following steps:

a) rotating the facial image, so that E _l and E _r connection

maintain standard. This ensures the consistency of the direction of the face, and reflects the rotation invariance of the face in the image plane;

b) Crop the face image according to a certain ratio. For example, set point O in the figure to

Midpoint, and

After cropping, in the 2d × 2d image, the point O can be guaranteed to be fixed at (0.5d, d). This ensures the consistency of the face position and reflects the translation invariance of the face in the image plane;

c) Reduce and enlarge the cropped image to obtain a face image with a uniform size and conforming to the standard. For example, if the size of the specified image is 128 × 128 pixels, that is to say

For a fixed length (64 pixels), the scaling factor is β = 2d / 128. This ensures the consistency of the size of the face, and reflects the scale invariance of the face in the image plane.

Step S14, the face image is allocated to the storage space corresponding to each task node of the second type through the master node, and all face attributes of the stored face images are triggered by all the task nodes of the second type Perform parallel calculations to obtain the calculation results of all face attributes.

In this embodiment, a deep learning framework (eg, TensorFlow framework) runs on each task node of the second type, and the deep learning framework calls the trained deep learning classifier model to calculate the face image to recognize Corresponding face attributes.

Taking the face attribute as "gender" as an example, the process of calculating gender using the deep learning framework includes:

a) Obtain the gender characteristic parameters of the face image and establish a parameter model;

Wherein, the gender characteristic parameters include hair (including beard) characteristic parameters, facial organ parameters, contour parameters and sexual characteristic characteristic parameters. Taking the need to obtain beard feature parameters as an example, first locate the facial feature points, and then perform skin color segmentation, use the active shape model algorithm to locate the facial feature points to obtain the chin area, and then use the skin color segmentation algorithm to separate the non-skin area of the chin. Finally, the beard color discrimination method is used to detect the beard in the non-skin area of the chin, and the parameters of the beard are extracted. Wherein, a characteristic value can be assigned to the beard in the face image according to the color of the beard, for example, an initial value can be assigned according to the color or density of the beard, so as to obtain the beard characteristic parameter according to the preset initial value.

In this embodiment, facial image features can be extracted and classified by using local binary pattern methods (Local Binary Patterns), neural network methods, and SVM (Support Vector Machine, support vector machine) methods to obtain the gender characteristics parameter.

b) Call a face gender classifier model;

c) Compare the parameter model with the face gender classifier model to identify the gender of the face image.

Step S15: Return the calculation result of the face attributes of each task node of the second type to the master node, and trigger the master node to summarize the calculation results of the face attributes of all people.

For example, when the calculation results of the second type of task nodes are respectively: gender is male, age is 18-20 years old, race is Han nationality, and expression is smile, then the master node can obtain the face image after being aggregated The calculation results of different face attributes.

In this embodiment, the calculation result of returning the face attribute of each task node of the second type to the master node includes:

a) Polling each task node of the second type through the master node, and sending a polling request message to each task node of the second type, so that when the task node of the second type receives the round Query request message and when at least one task node of the second type has finished calculating the face attribute, return the calculation result of the task node of the second type to the master node;

b) The master node regularly sends the polling request message to other task nodes of the second type that have not returned the calculation result, so that when the other task nodes of the second type have finished calculating the face attributes, The calculation result of the task node of the second type is returned to the master node.

In step S16, the summary result of the master node is output.

In this embodiment, the computer device includes a display screen, and the summary result is displayed on the display screen. The display screen may be a liquid crystal display (Liquid Crystal) (LCD) or an organic light-emitting diode (Organic Light-Emitting Diode, OLED) display. Of course, in other embodiments, the summary result can also be played through the microphone of the computer device.

The above-mentioned FIG. 1 details the face attribute recognition method of the present application. In the following, with reference to FIGS. 3-4, the functional modules of the software device implementing the face attribute recognition method and the hardware device implementing the face attribute recognition method The architecture is introduced.

It should be understood that the described embodiments are for illustrative purposes only and are not limited by this structure in the scope of patent applications.

FIG. 3 is a schematic structural diagram of a face attribute recognition device 300 according to a preferred embodiment of the present application. In some embodiments, the face attribute recognition device 300 runs on a computer device. The face attribute recognition device 300 may include a plurality of function modules composed of program code segments. The program codes of each program segment of the face attribute recognition device 300 may be stored in the memory of the computer device and executed by the at least one processor to implement the face attribute recognition function.

In this embodiment, the face attribute recognition device 10 may be divided into multiple functional modules according to the functions it performs. Referring to FIG. 3, the functional modules may include: a construction module 101, a distribution module 102, an extraction module 103, a return module 104, and an output module 105. The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, and are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

The building module 101 is used to build a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, and the building module 101 is also used to assign a corresponding to each task node Storage space in which all the task nodes are divided into a first type task node and at least two second type task nodes, and each second type task node corresponds to a face attribute.

The allocation module 102 is configured to acquire a target image through the master node, and allocate the target image to a storage space corresponding to the first-type task node, to trigger the first-type task node to the target The image is calculated to identify whether the target image includes a face image.

In this embodiment, before identifying whether the target image includes a face image, the allocation module 102 is also used to trigger the first type of task node to pre-process the target image to exclude environmental factors such as light Interference.

Further, the assignment module 102 triggers the first-type task node to scan whether the target image contains information about preset local features of a human face, when the image contains information about preset local features of a human face To determine that the target image includes a face image.

The extraction module 103 is configured to extract the face image from the target image through the first-type task node when the acquired target image includes a face image to implement living body detection and recognition. The return module 104 is used to return the face image extracted by the extraction module 103 to the master node.

In this embodiment, the extraction module 103 determines the number of face images included in the target image through the first type of task node, when the number of face images included in the target image is only one , The first type of task node delimits a rectangular border around the face image, and extracts the face image according to the rectangular border; when the number of face images included in the target image is at least two , The first type of task node delimits the rectangular boundary around each face image, calculates the area of the face image defined by each rectangular boundary, and selects one of the largest rectangular boundaries to extract the face image, Then, the extracted face image is subjected to normalization processing of geometric characteristics through the first-type task node, thereby extracting the face image from the target image. The normalization processing of the geometric characteristics can normalize the face image to the same position, angle and size. Since the distance between two eyes of a person is basically the same for most people, the position of the two eyes is usually used as the basis for geometric normalization of the face image.

Specifically, as shown in FIG. 2, assuming that the positions of the two eyes in the face image are El and Er, respectively, the extraction module 103 can realize the geometric normalization of the face image through the following steps:

a) rotating the facial image, so that E _l and E _r connection

Midpoint, and

The allocation module 102 is further configured to allocate the face image to the storage space corresponding to each second-type task node through the master node, and trigger all the second-type task nodes to store the face image The face attributes of all people are calculated in parallel, so as to obtain the calculation result of all face attributes.

Taking the face attribute as “gender” as an example, the distribution module 102 obtains the gender characteristic parameters of the face image and establishes a parameter model, calls a face gender classifier model, and compares the parameter model and the face gender The classifier models are compared to calculate the gender using the deep learning framework.

Wherein, the gender characteristic parameters include hair (including beard) characteristic parameters, facial organ parameters, contour parameters and sexual characteristic characteristic parameters. Taking the need to obtain beard feature parameters as an example, first locate the facial feature points, and then perform skin color segmentation, use the active shape model algorithm to locate the facial feature points to obtain the chin area, and then use the skin color segmentation algorithm to separate the chin non-skin color area. Finally, the beard color discrimination method is used to detect the beard in the non-skin area of the chin, and the parameters of the beard are extracted. Wherein, a characteristic value can be assigned to the beard in the face image according to the color of the beard, for example, an initial value can be assigned according to the color or density of the beard, so as to obtain the beard characteristic parameter according to the preset initial value.

The return module 104 is further used to return the calculation result of the face attributes of each task node of the second type to the master node, and trigger the master node to summarize the calculation results of the face attributes of all people.

In this embodiment, the return module 104 polls each task node of the second type through the master node, and sends a poll request message to each task node of the second type, so that when the When the polling request message is completed and at least one task node of the second type completes calculating the face attribute, the calculation result of the task node of the second type is returned to the master node, and the return module 104 further passes the The master node regularly sends the polling request message to other task nodes of the second type that have not returned the calculation result, so that the other task nodes of the second type send the request to the master node when the face attributes are calculated. The calculation result of the task node of the second type is returned.

The output module 105 is used to output the summary result of the master node.

As described above, the face attribute recognition device in the embodiment of the present application, through the parallel computing model, calculates different face attributes of the face image in parallel, which is beneficial to improve the operation efficiency; the subsequent face attribute recognition method It can be imported into various public occasions, such as stations, subways, shopping malls, and supermarkets, by acquiring the target images collected by the cameras of these occasions, and then recognizing various facial attributes, and comparing the recognition results with specific people (such as criminals) ) The recognition results of the face attributes are compared, so as to efficiently screen the target population according to the matching degree.

As shown in FIG. 4, FIG. 4 is a schematic structural diagram of a computer device 1 that implements the face attribute recognition method in a preferred embodiment of the present application. The computer device 1 includes a memory 20, a processor 30, and computer-readable instructions 40 stored in the memory 20 and executable on the processor 30, such as a face attribute recognition program.

When the processor 30 executes the computer-readable instructions 40, the steps of the face attribute recognition method in the above embodiment are implemented:

Among them, the parallel computing model may use a random access parallel machine (Parallel Random Access Machine, PRAM) model, an overall synchronous parallel computing model (Bulk Synchronous Parallel Computing Model, BSP), a LogP model and a block distributed storage (Block Distributed Model, BDM) One of the models. The main node of the parallel computing model is the input point of the target image to be recognized and the task assignment node TaskTrack. The task node is an operation point of face attributes, that is, a task execution node JobTrack. The first type of task node is used to calculate and identify whether the target image includes a face image. The second type of task node is used to calculate and recognize the face attribute of the face image when the target image includes a face image.

a) rotating the facial image, so that E _l and E _r connection

Midpoint, and

Wherein, the gender characteristic parameters include hair (including beard) characteristic parameters, facial organ parameters, contour parameters and sexual characteristic characteristic parameters. Taking the need to obtain beard feature parameters as an example, first locate the facial feature points, and then perform skin color segmentation, use the active shape model algorithm to locate the facial feature points to obtain the chin area, and then use the skin color segmentation algorithm to separate the chin non-skin color area Finally, the beard color discrimination method is used to detect the beard in the non-skin area of the chin, and the parameters of the beard are extracted. Wherein, a characteristic value can be assigned to the beard in the face image according to the color of the beard, for example, an initial value can be assigned according to the color or density of the beard, so as to obtain the beard characteristic parameter according to the preset initial value.

b) Call a face gender classifier model;

In step S16, the summary result of the master node is output.

Alternatively, when the processor 30 executes the computer-readable instruction 40, the functions of each module / unit in the embodiment of the face attribute recognition device described above, such as the units 101-105 in FIG. 3, are implemented.

Exemplarily, the computer-readable instructions 40 may be divided into one or more modules / units, the one or more modules / units are stored in the memory 20 and executed by the processor 30, To complete this application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions 40 in the computer device 1. For example, the computer-readable instructions 40 may be divided into a building module 101, an allocation module 102, an extraction module 103, a return module 104, and an output module 105 in FIG.

The computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server. A person skilled in the art may understand that the schematic diagram is only an example of the computer device 1 and does not constitute a limitation on the computer device 1, and may include more or less components than the illustration, or a combination of certain components, or different Components, for example, the computer device 1 may also include input and output devices, network access devices, buses, and the like.

The so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor 30 may also be any conventional processor, etc. The processor 30 is the control center of the computer device 1 and connects the entire computer device 1 using various interfaces and lines Various parts.

The memory 20 may be used to store the computer-readable instructions 40 and / or modules / units, and the processor 30 executes or executes the computer-readable instructions and / or modules / units stored in the memory 20, and The data stored in the memory 20 is called to realize various functions of the computer device 1. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one function required application programs (such as sound playback function, image playback function, etc.); the storage data area may Data (such as audio data, phone book, etc.) created according to the use of the computer device 1 is stored. In addition, the memory 20 may include a high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart, Media, Card, SMC), and a secure digital (SD) Card, flash memory card (Flash), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

If the module / unit integrated in the computer device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a non-volatile readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through computer-readable instructions. When reading the storage medium, the computer-readable instructions, when executed by the processor, can implement the steps of the foregoing method embodiments. Wherein, the computer readable instructions include computer readable instruction codes, and the computer readable instruction codes may be in source code form, object code form, executable file, or some intermediate form, etc. The non-volatile readable medium may include: any entity or device capable of carrying the computer-readable instruction code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the content contained in the non-volatile readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions. For example, in some jurisdictions, according to legislation and patent practice, non- Volatile readable media does not include electrical carrier signals and telecommunication signals.

In the several embodiments provided in this application, it should be understood that the disclosed computer device and method may be implemented in other ways. For example, the computer device embodiments described above are only schematic. For example, the division of the unit is only a logical function division, and there may be other division manners in actual implementation.

In addition, the functional units in the embodiments of the present application may be integrated in the same processing unit, or each unit may exist alone physically, or two or more units may be integrated in the same unit. The above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software function modules.

It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and is therefore intended to fall within the claims All changes within the meaning and scope of the equivalent requirements are included in this application. Any reference signs in the claims should not be considered as limiting the claims involved. In addition, it is clear that the word "include" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or computer devices stated in the computer device claims may also be implemented by the same unit or computer device through software or hardware. The first and second words are used to indicate names, but do not indicate any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the application is described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application Make modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present application.

Claims

A face attribute recognition method, characterized in that it includes:

Build a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, each task node is allocated a corresponding storage space, wherein all the task nodes are divided into one A first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute;

Acquiring a target image through the master node, and allocating the target image to the storage space corresponding to the first-type task node, triggering the first-type task node to calculate the target image to identify the target image Whether the target image includes a face image;

When the acquired target image includes a face image, extract the face image from the target image through the first-type task node, and return the extracted face image to the master node;

Assigning the face image to the storage space corresponding to each second-type task node through the master node, triggering all the second-type task nodes to perform parallel calculation on all face attributes of the stored face images To obtain the calculation result of all face attributes; and

Returning the calculation result of the face attributes of each task node of the second type to the master node, triggering the master node to summarize the calculation results of the face attributes of all people.
The face attribute recognition method according to claim 1, further comprising:

The summary result of the master node is output.
The face attribute recognition method according to claim 1, wherein the recognizing whether the target image includes a face image includes:

Trigger the first type of task node to scan whether the target image contains information of preset local features of a human face; and

When the image contains information on preset local features of a human face, it is determined that the target image includes a human face image.
The face attribute recognition method according to claim 1, wherein:

The extracting the face image from the target image through the first type of task node includes:

Judging the number of face images included in the target image through the first type of task node;

When the number of face images included in the target image is only one, the first type of task node delimits a rectangular boundary around the face image, and extracts the face image according to the rectangular boundary;

When the number of face images included in the target image is at least two, the rectangular boundaries surrounding each face image are respectively delineated by the first-type task nodes, and the face images defined by each rectangular boundary are calculated Area, and select one of the largest rectangular boundaries to extract the face image; and

The extracted face images are subjected to normalization of geometric characteristics through the first type of task nodes, so that the face images are normalized to the same position, angle and size.
The face attribute recognition method according to claim 1, wherein the calculation result of returning the face attribute of each task node of the second type to the master node includes:

Polling each task node of the second type through the master node, and sending a polling request message to each task node of the second type, so that when the task node of the second type receives the polling request Message and when at least one task node of the second type has calculated the face attribute, return the calculation result of the task node of the second type to the master node; and

The master node periodically sends the polling request message to the other second-type task nodes that have not returned the calculation result, so that the other second-type task nodes, after completing the calculation of the face attributes, The master node returns the calculation result of the task node of the second type.
The face attribute recognition method according to claim 1, wherein the face attributes include gender, age, race, and expression.
The face attribute recognition method according to claim 1, wherein the parallel computing model uses one of a random access parallel machine model, an overall synchronous parallel computing model, a LogP model, and a block distributed storage model.
A face attribute recognition device is characterized by comprising:

The building module is used to build a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, and a corresponding storage space is allocated to each task node, wherein all the tasks The node is divided into a first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute;

An allocation module, configured to acquire a target image through the master node, and allocate the target image to the storage space corresponding to the first-type task node, and trigger the first-type task node to perform Calculation to identify whether the target image includes a face image;

An extraction module, configured to extract the face image from the target image through the first-type task node when the acquired target image includes a face image; and

A return module, configured to return the face image extracted by the extraction module to the master node;

The allocating module is further configured to allocate the face image to the storage space corresponding to each second-type task node through the master node, and trigger all the second-type task nodes to store the stored face image All face attributes are calculated in parallel to obtain the calculation result of all face attributes; and

The returning module is further used to return the calculation result of the face attributes of each task node of the second type to the master node, and trigger the master node to summarize the calculation results of the face attributes of all people.
A computer device, characterized by comprising a processor and a memory, wherein the memory stores at least one computer-readable instruction, and the processor executes the computer-readable instruction to implement the following steps:

Build a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, each task node is allocated a corresponding storage space, wherein all the task nodes are divided into one A first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute;

Acquiring a target image through the master node, and allocating the target image to the storage space corresponding to the first-type task node, triggering the first-type task node to calculate the target image to identify the target image Whether the target image includes a face image;

When the acquired target image includes a face image, extract the face image from the target image through the first-type task node, and return the extracted face image to the master node;

Assigning the face image to the storage space corresponding to each second-type task node through the master node, triggering all the second-type task nodes to perform parallel calculation on all face attributes of the stored face images To obtain the calculation result of all face attributes; and

Returning the calculation result of the face attributes of each task node of the second type to the master node, triggering the master node to summarize the calculation results of the face attributes of all people.
The computer device of claim 9, wherein the processor is further configured to implement the following steps when executing the computer-readable instructions:

The summary result of the master node is output.
The computer device of claim 9, wherein the processor executes the computer-readable instructions to realize the following steps when recognizing whether the target image includes a face image.

Trigger the first type of task node to scan whether the target image contains information of preset local features of a human face; and

When the image contains information on preset local features of a human face, it is determined that the target image includes a human face image.
The computer device according to claim 9, wherein the processor executes the computer-readable instructions to extract the face image from the target image through the task node of the first type to implement The following steps:

Judging the number of face images included in the target image through the first type of task node;

When the number of face images included in the target image is only one, the first type of task node delimits a rectangular boundary around the face image, and extracts the face image according to the rectangular boundary;

When the number of face images included in the target image is at least two, the rectangular boundaries surrounding each face image are respectively delineated by the first-type task nodes, and the face images defined by each rectangular boundary are calculated Area, and select one of the largest rectangular boundaries to extract the face image; and

The first-type task node normalizes the geometric characteristics of the extracted face image, so that the face image is normalized to the same position, angle and size.
The computer device according to claim 9, wherein the processor executes the computer-readable instructions to return to the master node the calculation result of the face attribute of each task node of the second type The following steps:

Polling each task node of the second type through the master node, and sending a polling request message to each task node of the second type, so that when the task node of the second type receives the polling request Message and when at least one task node of the second type has calculated the face attribute, return the calculation result of the task node of the second type to the master node; and

The master node periodically sends the polling request message to the other second-type task nodes that have not returned the calculation result, so that the other second-type task nodes, after completing the calculation of the face attributes, The master node returns the calculation result of the task node of the second type.
The computer device according to claim 9, wherein the face attributes include gender, age, race, and expression.
A non-volatile readable storage medium is characterized in that at least one computer-readable instruction is stored on the non-volatile readable storage medium, and when the computer-readable instruction is executed by a processor, the following steps are implemented:

Build a parallel computing model, the parallel computing model includes a master node and a plurality of task nodes connected to the master node, each task node is allocated a corresponding storage space, wherein all the task nodes are divided into one A first type task node and at least two second type task nodes, each second type task node corresponds to a face attribute;

Acquiring a target image through the master node, and allocating the target image to the storage space corresponding to the first-type task node, triggering the first-type task node to calculate the target image to identify the target image Whether the target image includes a face image;

When the acquired target image includes a face image, extract the face image from the target image through the first-type task node, and return the extracted face image to the master node;

Assigning the face image to the storage space corresponding to each second-type task node through the master node, triggering all the second-type task nodes to perform parallel calculation on all face attributes of the stored face images To obtain the calculation result of all face attributes; and

Returning the calculation result of the face attributes of each task node of the second type to the master node, triggering the master node to summarize the calculation results of the face attributes of all people.
The storage medium according to claim 15, wherein the computer-readable instructions are further used to implement the following steps when executed by the processor:

The summary result of the master node is output.
The storage medium of claim 15, wherein when recognizing whether the target image includes a face image, the computer-readable instructions are executed by the processor to implement the following steps:

Trigger the first type of task node to scan whether the target image contains information of preset local features of a human face; and

When the image contains information on preset local features of a human face, it is determined that the target image includes a human face image.
The storage medium of claim 15, wherein the computer readable instructions are executed by the processor when the face image is extracted from the target image by the first type of task node Implement the following steps:

Judging the number of face images included in the target image through the first type of task node;

When the number of face images included in the target image is only one, a rectangular boundary surrounding the face image is delineated by the first-type task node, and the face image is extracted according to the rectangular boundary;

When the number of face images included in the target image is at least two, the rectangular boundaries surrounding each face image are respectively delineated by the first-type task nodes, and the face images defined by each rectangular boundary are calculated Area, and select one of the largest rectangular boundaries to extract the face image; and

The first-type task node normalizes the geometric characteristics of the extracted face image, so that the face image is normalized to the same position, angle and size.
The storage medium according to claim 15, wherein the computer readable instructions are executed by the processor when returning the calculation result of the face attribute of each task node of the second type to the master node Implement the following steps:

Polling each task node of the second type through the master node, and sending a polling request message to each task node of the second type, so that when the task node of the second type receives the polling request Message and when at least one task node of the second type has calculated the face attribute, return the calculation result of the task node of the second type to the master node; and

The master node periodically sends the polling request message to the other second-type task nodes that have not returned the calculation result, so that the other second-type task nodes, after completing the calculation of the face attributes, The master node returns the calculation result of the task node of the second type.
The storage medium of claim 15, wherein the face attributes include gender, age, race, and expression.