WO2021056531A1

WO2021056531A1 - Face gender recognition method, face gender classifier training method and device

Info

Publication number: WO2021056531A1
Application number: PCT/CN2019/109014
Authority: WO
Inventors: 张欢欢
Original assignee: 京东方科技集团股份有限公司
Priority date: 2019-09-29
Filing date: 2019-09-29
Publication date: 2021-04-01
Also published as: CN110785769A

Abstract

A face gender recognition method, and a face gender classifier training method and device, wherein the face gender recognition method comprises: performing face detection on an image to be recognized, and acquiring a target face in the image to be recognized (11); performing face feature point extraction on the target face (12); obtaining a face region of interest image according to the face feature point (13); performing feature extraction on the face region of interest image to obtain a feature image (14); performing grayscale compression processing on the feature image, and obtaining a grayscale histogram of the grayscale compressed image (15); obtaining a gender feature of the target face according to the grayscale histogram (16); and inputting the gender feature of the target human face into the trained face gender classifier to obtain a gender recognition result of the target face (17).

Description

Face gender recognition method, training method and device of face gender classifier

Technical field

The present disclosure relates to the field of image processing technology, and in particular to a method for facial gender recognition, a method and device for training a facial gender classifier.

Background technique

Human face is a very important biological feature of human beings. Face-based recognition function has gradually become the focus of research in recent years. Face gender recognition has broad development prospects and application value in pattern recognition, artificial intelligence, computer vision, information security, etc.

Because the difference between the facial features of men and women is extremely subtle, coupled with the influence of factors such as human expression, age, and light, it is more difficult to identify gender by face, and it is difficult to achieve the desired accuracy.

Summary of the invention

The present disclosure provides a face gender recognition method, a face gender classifier training method and device.

In the first aspect, the present disclosure provides a face gender recognition method, including:

Performing face detection on the image to be recognized, and acquiring the target face in the image to be recognized;

Performing face feature point extraction on the target face;

Obtaining an image of a region of interest of a human face according to the facial feature points;

Performing feature extraction on the image of the region of interest of the face to obtain a feature image;

Performing gray-scale compression processing on the characteristic image, and obtaining a gray-scale histogram of the image after the gray-scale compression;

Obtaining the gender feature of the target face according to the grayscale histogram;

The gender feature of the target face is input into the trained face gender classifier to obtain the gender recognition result of the target face.

Optionally, after extracting the facial feature points of the target face, before obtaining the image of the region of interest of the face according to the facial feature points, the method further includes:

According to the facial feature points, geometrically correct the target face to obtain a corrected image. In the corrected image, the line between the two eyes of the target face is a horizontal line.

Optionally, the performing feature extraction on the image of the region of interest of the face to obtain a feature image also includes:

The image of the region of interest of the face is scaled to a specified size.

Optionally, the performing feature extraction on the facial region of interest image to obtain a feature image includes: using a symbiotic local binary pattern feature extraction algorithm to perform feature extraction on the facial region of interest image to obtain multiple Feature images;

The obtaining the gender feature of the target face according to the gray-scale histogram includes: connecting the gray-scale histograms of the multiple feature images to obtain the gender feature of the target face.

Optionally, the performing gray-scale compression processing on the characteristic image and obtaining the gray-scale histogram of the gray-scale compressed image includes:

Divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2;

For at least one of the image blocks, compress the gray level of the image block to a first value to obtain a gray level compressed image, where the first value is less than the gray level of the characteristic image;

Obtain a grayscale histogram of the image after grayscale compression;

Connect the gray histograms of the n×m image blocks to obtain the gray histogram of the characteristic image.

In the second aspect, the present disclosure provides a method for training a face gender classifier, including:

Acquiring multiple face image samples, where the multiple face image samples include multiple images with male faces and multiple images with female faces;

Performing face detection on the face image sample, and determining the position of the target face in the face image sample;

Performing face feature point extraction on the target face;

The face gender classifier is trained by using the gender features of the target face in all the face image samples to obtain a trained face gender classifier.

The image of the region of interest of the face is scaled to a specified size.

Optionally, the performing feature extraction on the face region of interest image to obtain a feature image includes: using a CoLBP feature extraction algorithm to perform feature extraction on the face region of interest image to obtain multiple feature images;

Obtain a grayscale histogram of the image after grayscale compression;

In a third aspect, the present disclosure provides a face gender recognition device, including:

A face detector, configured to perform face detection on the image to be recognized, and obtain the target face in the image to be recognized;

A face feature point extractor for extracting face feature points on the target face;

A face region of interest obtainer, configured to obtain an image of a face region of interest according to the facial feature points;

An image feature extractor, which is used to perform feature extraction on the image of the region of interest of the face to obtain a feature image;

A gray-scale compression processor, configured to perform gray-scale compression processing on the characteristic image, and obtain a gray-scale histogram of the image after the gray-scale compression;

A gender feature obtainer, configured to obtain the gender feature of the target face according to the grayscale histogram;

The gender recognizer is used to input the gender characteristics of the target face into the trained face gender classifier to obtain the gender recognition result of the target face.

In a fourth aspect, the present disclosure provides a training device for a face gender classifier, including:

A face image sample acquirer, configured to acquire multiple face image samples, the multiple face image samples including multiple images with male faces and multiple images with female faces;

The trainer is used to train the face gender classifier by using the gender characteristics of the target face in all the face image samples to obtain the trained face gender classifier.

In a fifth aspect, the present disclosure provides a face gender recognition device, including a processor, a memory, and a computer program stored on the memory and running on the processor, and the computer program is executed by the processor. The steps of the above face gender recognition method are realized during execution.

In a sixth aspect, the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned face gender recognition method are realized; or, When the computer program is executed by the processor, the steps of the training method of the face gender classifier are realized.

Description of the drawings

By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only used for the purpose of illustrating the preferred embodiments, and are not considered as a limitation to the present disclosure. Also, throughout the drawings, the same reference symbols are used to denote the same components. In the attached picture:

FIG. 1 is a schematic flowchart of a face gender recognition method provided by some embodiments of the present disclosure;

FIG. 2 is a schematic flowchart of obtaining a grayscale histogram of an image after grayscale compression according to some embodiments of the present disclosure;

3 is a schematic flowchart of a method for training a face gender classifier provided by some embodiments of the present disclosure;

FIG. 4 is a schematic flowchart of obtaining a grayscale histogram of an image after grayscale compression according to some other embodiments of the present disclosure;

FIG. 5 is a schematic structural diagram of a face gender recognition device provided by some embodiments of the present disclosure;

FIG. 6 is a schematic structural diagram of a grayscale compression processor provided by some embodiments of the present disclosure;

FIG. 7 is a schematic structural diagram of a training device for a face gender classifier provided by some embodiments of the present disclosure;

FIG. 8 is a schematic structural diagram of a gray scale compression processor provided by some other embodiments of the present disclosure;

FIG. 9 is another schematic structural diagram of a face gender recognition device provided by some embodiments of the present disclosure;

FIG. 10 is a schematic diagram of another structure of a training device for a face gender classifier provided by some embodiments of the present disclosure.

detailed description

The technical solutions in the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a face gender recognition method provided by some embodiments of the present disclosure. The method includes:

Step 11: Perform face detection on the image to be recognized, and obtain the target face in the image to be recognized;

Face detection is to search for a face from an image and determine the position and size of the face.

In some embodiments, the Dlib library can be used for face detection. Dlib is a toolbox that contains machine learning algorithms and tools for creating complex software to solve real-world problems. The Dlib library includes tools for object detection in images, including frontal face detection and object pose estimation. Of course, the present disclosure can also use other algorithms for face detection.

Step 12: Perform face feature point extraction on the target face;

In some embodiments, the Dlib library can be used to extract 68 facial feature points. Of course, if the present disclosure adopts other algorithms for face detection, the number of facial feature points extracted in the present disclosure may be other numbers, which is not limited here.

Step 13: Obtain a region of interest (ROI) image of the face according to the feature points of the face;

Step 14: Perform feature extraction on the image of the region of interest of the face to obtain a feature image;

Step 15: Perform gray-scale compression processing on the characteristic image, and obtain a gray-scale histogram of the image after gray-scale compression;

Assuming that the gray level of the feature image before compression is 256, that is, the range of gray value is 0 to 255, the feature image is subjected to gray level compression processing, and the gray level of the compressed image is less than 256, for example, it is 16, that is, the image's gray level is less than 256. The range of gray value is 0-15. The gray level in the present disclosure can be in other ranges, which are not limited here.

Step 16: Obtain the gender characteristics of the target face according to the grayscale histogram;

Step 17: Input the gender characteristics of the target face into the trained face gender classifier to obtain the gender recognition result of the target face.

In the present disclosure, the feature image is subjected to gray-scale compression processing to reduce the dimensionality of the features in the feature image, thereby reducing the influence of the age, expression, and illumination of the target face on gender recognition, improving the recognition accuracy, and making The face gender recognition method is not only suitable for laboratory scenes, but can also be used in natural environments and other scenes. At the same time, due to the feature image dimensionality reduction, the recognition speed can also be effectively improved, and it is suitable for devices that require high recognition speed such as embedded devices without GPU (graphics processing unit).

In some embodiments, optionally, after extracting facial feature points on the target face in step 12, before obtaining a face region of interest image based on the facial feature points in step 13 , It also includes: preprocessing the image to be recognized.

In some embodiments, the preprocessing may include: filtering the image to be recognized. Image filtering is to suppress image noise while preserving the details of the image as much as possible. The quality of its processing effect will directly affect the effectiveness and reliability of subsequent image processing and analysis. In some embodiments, a Gaussian filter may be used to filter the image to be recognized to remove image noise.

In some embodiments, the preprocessing may include: performing geometric correction on the target face according to the facial feature points to obtain a corrected image, and in the corrected image, the target face The line between the two eyes is a horizontal line.

Optionally, the affine transformation matrix may be determined according to the feature points of the face, and the geometric correction of the target face may be performed according to the affine transformation matrix.

The target face detected from the image to be recognized may have a tilt problem. Therefore, the target face needs to be geometrically corrected so that the line between the two eyes in the corrected target face is a horizontal line, which can be used for subsequent The feature extraction process plays an optimization role.

After performing geometric correction, it is also necessary to obtain the coordinates of the feature points of the face in the corrected image to determine the region of interest of the face. Optionally, the highest point, lowest point, leftmost point, and rightmost point in the coordinates of the feature points of the face can be obtained, and the circumscribed matrix of the face can be determined, and a screenshot of the circumscribed matrix area can be taken to obtain the area of interest of the face image.

In some embodiments, optionally, performing feature extraction on the facial region of interest image in step 14 above to obtain a feature image also includes: scaling the facial region of interest image to a specified size. . That is, down-sampling the image of the area of interest of the face to make the image of the area of interest of the face have a fixed size, thereby reducing the impact of factors such as image resolution and the distance between the camera and the face on the face gender recognition, and improving the recognition method Robustness.

In some embodiments, optionally, performing feature extraction on the image of the region of interest of the face in step 14 to obtain a feature image includes: using a CoLBP (Co-occurrence Local Binary Pattern) feature extraction algorithm to perform Feature extraction is performed on the image of the region of interest of the face to obtain multiple feature images. For example, 8 feature images are obtained. The number of feature images obtained in the present disclosure may be other numbers, which is not limited here.

At this time, the obtaining the gender characteristics of the target face according to the gray-level histogram in step 16 includes: connecting the gray-scale histograms of the multiple feature images to obtain the gender characteristics of the target face .

Compared with the existing CNN (Convolutional Neural Network) feature extraction algorithm, the CoLBP feature extraction algorithm has greatly reduced computational complexity, which can effectively improve the recognition speed. It is suitable for the recognition speed requirements of embedded devices without GPU. Higher equipment.

The CoLBP feature extraction algorithm can be specifically as follows: first use multiple directional filters to extract the edge response feature images of the face region of interest image in multiple directions; and then obtain the LBP( Local binary mode) feature to obtain multiple feature images. For example, eight directional filters are used to extract the edge response feature images of the image of the region of interest in the face in multiple directions; then, the edge response feature images in the eight directions are respectively calculated for LBP features to obtain multiple feature images. The eight directions may respectively include two directions on the horizontal center line of the image, two directions on the vertical center line, and four directions on the diagonal line. The use of 8 directional filters can provide more refined image edge response characteristics. Of course, the present disclosure does not exclude the use of 4 directional filters or more than 8 directional filters.

In some embodiments, optionally, please refer to FIG. 2. Performing gray-scale compression processing on the feature image in step 15 above and obtaining a gray-scale histogram of the image after gray-scale compression includes:

Step 151: Divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2; for example, n is 7, the value of m N can be the same or different.

Step 152: For at least one of the image blocks, compress the gray level of the image block to a first value to obtain a gray level compressed image, where the first value is less than the gray level of the characteristic image level;

The gray level of the characteristic image is, for example, 256, and the first value is, for example, 16.

Step 153: Obtain a grayscale histogram of the image after grayscale compression;

Optionally, the grayscale histogram is a normalized histogram.

Step 154: Connect the gray histograms of the n×m image blocks to obtain the gray histogram of the characteristic image.

Optionally, the grayscale histograms of each image block of a feature image can be connected in the order from left to right and top to bottom to obtain the grayscale histogram of the feature image.

If the number of feature images is multiple, for example, eight, then the gray histograms of the eight feature images are connected to obtain the gender feature of the target face.

Please refer to FIG. 3. FIG. 3 is a schematic flowchart of a method for training a face gender classifier according to some embodiments of the present disclosure. The method includes:

Step 21: Obtain multiple face image samples, where the multiple face image samples include multiple images with male faces and multiple images with female faces;

Optionally, the number of images with male faces and images with female faces may be the same or approximately the same.

Optionally, the images with male faces and the images with female faces include images of people of different age groups, so that the obtained face gender classifier can identify people of different age groups.

Step 22: Perform face detection on the face image sample, and determine the position of the target face in the face image sample;

In some embodiments, the Dlib library can be used for face detection.

Step 23: Perform face feature point extraction on the target face;

In some embodiments, the Dlib library can be used to extract 68 facial feature points. The number of facial feature points extracted in the present disclosure can be other numbers, which is not limited here.

Step 24: Obtain an image of the region of interest of the face according to the feature points of the face;

Step 25: Perform feature extraction on the image of the region of interest of the face to obtain a feature image;

Step 26: Perform gray-scale compression processing on the characteristic image, and obtain a gray-scale histogram of the image after the gray-scale compression;

Step 27: Obtain the gender characteristics of the target face according to the grayscale histogram;

Step 28: Use the gender features of the target face in all the face image samples to train the face gender classifier to obtain a trained face gender classifier.

In the present disclosure, the feature image is subjected to gray-scale compression processing to reduce the dimensionality of the features in the feature image, thereby reducing the influence of factors such as age, expression, and lighting of the target face on gender recognition, improving the recognition accuracy, so that The face gender classifier is not only suitable for laboratory scenes, but can also be used in natural environments and other scenes.

In some embodiments, optionally, after the face feature points are extracted on the target face in step 23, before the face region of interest image is obtained according to the face feature points in step 24, , It also includes: preprocessing the face image sample.

In some embodiments, the preprocessing may include: filtering the face image samples. Image filtering is to suppress image noise while preserving the details of the image as much as possible. The quality of its processing effect will directly affect the effectiveness and reliability of subsequent image processing and analysis. In some embodiments, a Gaussian filter may be used to filter the face image samples to remove image noise.

The target face detected from the face image sample may have the problem of tilt. Therefore, the target face needs to be geometrically corrected so that the line between the two eyes in the corrected target face is a horizontal line, which can be corrected. The subsequent feature extraction process plays an optimization role.

In some embodiments, optionally, performing feature extraction on the facial region of interest image in step 25 above to obtain a feature image also includes: scaling the facial region of interest image to a specified size. . That is, the image of the region of interest of the face is down-sampled to make the image of the region of interest of the face a fixed size, thereby reducing the impact of factors such as image resolution and the distance between the camera and the face on the face gender recognition, and improving the face Robustness of gender classifiers.

In some embodiments, optionally, performing feature extraction on the face region of interest image in step 25 to obtain a feature image includes: using a CoLBP feature extraction algorithm to perform feature extraction on the face region of interest image Perform feature extraction to obtain multiple feature images. For example, 8 feature images are obtained. The number of feature images obtained in the present disclosure may be other numbers, which is not limited here.

At this time, in the above step 27, obtaining the gender characteristics of the target face according to the grayscale histogram includes: connecting the grayscale histograms of the multiple feature images to obtain the gender characteristics of the target face .

Compared with the existing CNN feature extraction algorithm, the CoLBP feature extraction algorithm has a greatly reduced computational complexity, which can effectively improve the recognition speed. It is suitable for devices that require high recognition speed such as embedded devices without GPU.

The CoLBP feature extraction algorithm can be specifically as follows: first use multiple directional filters to extract the edge response feature images of the face region of interest image in multiple directions; and then obtain the LBP features of the edge response feature images in the multiple directions. , Get multiple feature images.

In some embodiments, optionally, referring to FIG. 4, performing gray-scale compression processing on the feature image in step 26 and obtaining a gray-scale histogram of the image after gray-scale compression includes:

Step 261: Divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2; for example, n is 7, the value of m It can be the same as n or different.

Step 262: For at least one of the image blocks, compress the gray level of the image block to a first value to obtain a gray level compressed image, where the first value is less than the gray level of the characteristic image level;

Step 263: Obtain a grayscale histogram of the image after grayscale compression;

Optionally, the grayscale histogram is a normalized histogram.

Step 264: Connect the gray histograms of the n×m image blocks to obtain the gray histogram of the characteristic image.

In some embodiments, optionally, SVM (Support Vector Machine) may be used in step 28 to train the gender characteristics of the target face in all face image samples to obtain a trained face gender classifier. Optionally, the kernel function of the SVM adopts a linear kernel function.

Please refer to FIG. 5. FIG. 5 is a schematic structural diagram of the face gender recognition device of the present disclosure. The face gender recognition device 30 includes:

The face detector 31 is configured to perform face detection on the image to be recognized, and obtain the target face in the image to be recognized;

The face feature point extractor 32 is configured to perform face feature point extraction on the target face;

The face interest area obtainer 33 is configured to obtain an image of the face interest area according to the face feature points;

The image feature extractor 34 is configured to perform feature extraction on the image of the region of interest of the face to obtain a feature image;

The gray level compression processor 35 is configured to perform gray level compression processing on the characteristic image, and obtain a gray level histogram of the image after the gray level compression;

The gender feature obtainer 36 is configured to obtain the gender feature of the target face according to the gray histogram;

The gender recognizer 37 is configured to input the gender characteristics of the target face into the trained face gender classifier to obtain the gender recognition result of the target face.

Optionally, the face gender recognition device of the present disclosure further includes:

The corrector is used to perform geometric correction on the target face according to the face feature points to obtain a corrected image. In the corrected image, the connection between the two eyes of the target face The line is a horizontal line.

The scaler is used to scale the image of the region of interest of the face to a specified size.

Optionally, the image feature extractor 34 is configured to use the CoLBP feature extraction algorithm to perform feature extraction on the image of the region of interest of the face to obtain multiple feature images; for example, to obtain 8 feature images. The number of feature images obtained in the present disclosure may be other numbers, which is not limited here.

The gender feature obtainer 36 is used to connect the gray histograms of the multiple feature images to obtain the gender feature of the target face.

Optionally, referring to FIG. 6, the gray scale compression processor 35 includes:

The dividing unit 351 is configured to divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2;

The compression unit 352 is configured to compress the gray scale of the image block to a first value for at least one of the image blocks to obtain a gray scale compressed image, where the first value is smaller than the characteristic image The gray level;

The obtaining unit 353 is configured to obtain the grayscale histogram of the grayscale compressed image;

The connecting unit 354 is configured to connect the grayscale histograms of the n×m image blocks to obtain the grayscale histogram of the characteristic image.

Each device in the face gender recognition device in the foregoing embodiment and the unit modules included in each device can be implemented by hardware, for example, by a hardware circuit.

Please refer to FIG. 7. FIG. 7 is a schematic structural diagram of a training device for a face gender classifier of the present disclosure. The training device 40 for a face gender classifier includes:

The face image sample acquirer 41 is configured to acquire multiple face image samples, where the multiple face image samples include multiple images with male faces and multiple images with female faces;

The face detector 42 is configured to perform face detection on the face image sample, and determine the position of the target face in the face image sample;

The face feature point extractor 43 is configured to perform face feature point extraction on the target face;

The face region of interest obtainer 44 is configured to obtain an image of the face region of interest according to the facial feature points;

The image feature extractor 45 is configured to perform feature extraction on the image of the region of interest of the face to obtain a feature image;

The gray level compression processor 46 is configured to perform gray level compression processing on the characteristic image, and obtain a gray level histogram of the image after the gray level compression;

The gender feature obtainer 47 is configured to obtain the gender feature of the target face according to the grayscale histogram;

The trainer 48 is configured to train the face gender classifier by using the gender characteristics of the target face in all the face image samples to obtain the trained face gender classifier.

Optionally, the training device for the face gender classifier of the present disclosure further includes:

Optionally, the image feature extractor 43 is configured to use the CoLBP feature extraction algorithm to perform feature extraction on the image of the region of interest of the face to obtain multiple feature images; for example, to obtain 8 feature images. The number of feature images obtained in the present disclosure may be other numbers, which is not limited here.

The gender feature obtainer 45 is used to connect the gray histograms of the multiple feature images to obtain the gender feature of the target face.

Optionally, referring to FIG. 8, the gray-scale compression processor 44 includes:

The dividing unit 441 is configured to divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2;

The compression unit 442 is configured to compress the gray scale of the image block to a first value for at least one of the image blocks to obtain a gray scale compressed image, where the first value is smaller than the characteristic image The gray level;

The obtaining unit 443 is configured to obtain the grayscale histogram of the grayscale compressed image;

The connecting unit 444 is configured to connect the gray histograms of the n×m image blocks to obtain the gray histogram of the characteristic image.

The various components of the training device for the face gender classifier in the foregoing embodiment and the unit modules included in each component can be implemented in a hardware manner, for example, through a hardware circuit.

Please refer to FIG. 9, which is another structural diagram of the facial gender recognition device of the present disclosure. The facial gender recognition device 50 includes a processor 51, a memory 52, and is stored in the memory 112 and can be stored in the processor. The computer program running on 51 implements the following steps when the computer program is executed by the processor 51:

Performing face feature point extraction on the target face;

Optionally, the following steps may also be implemented when the computer program is executed by the processor 51:

After the face feature point extraction is performed on the target face, before obtaining the face region of interest image according to the face feature points, the method further includes:

The feature extraction on the image of the region of interest of the face to obtain a feature image also includes:

The image of the region of interest of the face is scaled to a specified size.

The performing feature extraction on the image of the region of interest of the face to obtain a feature image includes: using a symbiotic local binary pattern feature extraction algorithm to perform feature extraction on the image of the region of interest of the face to obtain multiple feature images;

The performing gray-scale compression processing on the characteristic image and obtaining the gray-scale histogram of the image after the gray-scale compression includes:

Obtain a grayscale histogram of the image after grayscale compression;

Please refer to FIG. 10, which is another structural diagram of the training device for the face gender classifier of the present disclosure. The face gender recognition device 60 includes a processor 61, a memory 62, and is stored on the memory 112 and can be The computer program running on the processor 61 implements the following steps when the computer program is executed by the processor 61:

Performing face feature point extraction on the target face;

Optionally, the following steps may also be implemented when the computer program is executed by the processor 61:

The image of the region of interest of the face is scaled to a specified size.

The performing feature extraction on the image of the region of interest of the human face to obtain a feature image includes: using a CoLBP feature extraction algorithm to perform feature extraction on the image of the region of interest of the human face to obtain multiple feature images;

Obtain a grayscale histogram of the image after grayscale compression;

The present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned face gender recognition method embodiment is realized, and can achieve The same technical effect, in order to avoid repetition, will not be repeated here.

The present disclosure also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned method for training a face gender classifier is realized, And can achieve the same technical effect, in order to avoid repetition, I will not repeat them here.

Wherein, the computer-readable storage medium, such as read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk, or optical disk, etc.

It should be noted that in this article, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, It also includes other elements not explicitly listed, or elements inherent to the process, method, article, or device. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or device that includes the element.

Through the description of the above implementation manners, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is better.的实施方式。 Based on this understanding, the technical solution of the present disclosure essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes several instructions to make a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present disclosure.

The embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are only illustrative and not restrictive. Those of ordinary skill in the art are Under the enlightenment of the present disclosure, without departing from the purpose of the present disclosure and the scope of protection of the claims, many forms can be made, all of which fall within the protection of the present disclosure.

Claims

A face gender recognition method, including:

Performing face detection on the image to be recognized, and acquiring the target face in the image to be recognized;

Performing face feature point extraction on the target face;

Obtaining an image of a region of interest of a human face according to the facial feature points;

Performing feature extraction on the image of the region of interest of the face to obtain a feature image;

Performing gray-scale compression processing on the characteristic image, and obtaining a gray-scale histogram of the image after the gray-scale compression;

Obtaining the gender feature of the target face according to the grayscale histogram;

The gender feature of the target face is input into the trained face gender classifier to obtain the gender recognition result of the target face.
The method according to claim 1, wherein, after extracting the facial feature points of the target face, before obtaining the image of the region of interest of the face according to the facial feature points, the method further comprises:

According to the facial feature points, geometrically correct the target face to obtain a corrected image. In the corrected image, the line between the two eyes of the target face is a horizontal line.
The method according to claim 1, wherein said performing feature extraction on the image of the region of interest of the face to obtain a feature image further comprises:

The image of the region of interest of the face is scaled to a specified size.
The method of claim 1, wherein:

The performing feature extraction on the image of the region of interest of the face to obtain a feature image includes: using a symbiotic local binary pattern feature extraction algorithm to perform feature extraction on the image of the region of interest of the face to obtain multiple feature images;

The obtaining the gender feature of the target face according to the gray-scale histogram includes: connecting the gray-scale histograms of the multiple feature images to obtain the gender feature of the target face.
The method according to claim 1, wherein the performing gray-scale compression processing on the characteristic image and obtaining a gray-scale histogram of the image after the gray-scale compression includes:

Divide the characteristic image into n×m image blocks of the same size, where n is a positive integer greater than or equal to 2, and m is a positive integer greater than or equal to 2;

For at least one of the image blocks, compress the gray level of the image block to a first value to obtain a gray level compressed image, where the first value is less than the gray level of the characteristic image;

Obtain a grayscale histogram of the image after grayscale compression;

Connect the gray histograms of the n×m image blocks to obtain the gray histogram of the characteristic image.
A method for training a face gender classifier, which includes:

Acquiring multiple face image samples, where the multiple face image samples include multiple images with male faces and multiple images with female faces;

Performing face detection on the face image sample, and determining the position of the target face in the face image sample;

Performing face feature point extraction on the target face;

Obtaining an image of a region of interest of a human face according to the facial feature points;

Performing feature extraction on the image of the region of interest of the face to obtain a feature image;

Performing gray-scale compression processing on the characteristic image, and obtaining a gray-scale histogram of the image after the gray-scale compression;

Obtaining the gender feature of the target face according to the grayscale histogram;

The face gender classifier is trained by using the gender features of the target face in all the face image samples to obtain a trained face gender classifier.
A face gender recognition device, which includes:

A face detector, configured to perform face detection on the image to be recognized, and obtain the target face in the image to be recognized;

A face feature point extractor for extracting face feature points on the target face;

A face region of interest obtainer, configured to obtain an image of a face region of interest according to the facial feature points;

An image feature extractor, which is used to perform feature extraction on the image of the region of interest of the face to obtain a feature image;

A gray-scale compression processor, configured to perform gray-scale compression processing on the characteristic image, and obtain a gray-scale histogram of the image after the gray-scale compression;

A gender feature obtainer, configured to obtain the gender feature of the target face according to the grayscale histogram;

The gender recognizer is used to input the gender characteristics of the target face into the trained face gender classifier to obtain the gender recognition result of the target face.
A training device for a face gender classifier, which includes:

A face image sample acquirer, configured to acquire multiple face image samples, the multiple face image samples including multiple images with male faces and multiple images with female faces;

A face detector, configured to perform face detection on the face image sample, and determine the position of the target face in the face image sample;

A face feature point extractor for extracting face feature points on the target face;

A face region of interest obtainer, configured to obtain an image of a face region of interest according to the facial feature points;

An image feature extractor, which is used to perform feature extraction on the image of the region of interest of the face to obtain a feature image;

A gray-scale compression processor, configured to perform gray-scale compression processing on the characteristic image, and obtain a gray-scale histogram of the image after the gray-scale compression;

A gender feature obtainer, configured to obtain the gender feature of the target face according to the grayscale histogram;

The trainer is used to train the face gender classifier by using the gender characteristics of the target face in all the face image samples to obtain the trained face gender classifier.
A face gender recognition device, which includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor. When the computer program is executed by the processor, the implementation is as claimed in the claims. Steps of the face gender recognition method described in any one of 1 to 5.
A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the face gender recognition method according to any one of claims 1 to 5 is realized Or, when the computer program is executed by a processor, the steps of the method for training a face gender classifier according to claim 6 are realized.