WO2019127262A1

WO2019127262A1 - Cloud end-based human face in vivo detection method, electronic device and program product

Info

Publication number: WO2019127262A1
Application number: PCT/CN2017/119543
Authority: WO
Inventors: 刘兆祥; 廉士国; 王敏
Original assignee: 深圳前海达闼云端智能科技有限公司
Priority date: 2017-12-28
Filing date: 2017-12-28
Publication date: 2019-07-04
Also published as: CN108124486A

Abstract

Provided are a cloud end-based human face in vivo detection method, an electronic device and a program product, wherein same are applied to the technical field of human face detection. The method comprises: consecutively collecting multiple first human face images of a user; recognizing whether there is a micro action in the multiple consecutive first human face images after it is determined that each of the first human face images is an in vivo image; and if there is a micro action, confirming that human face in vivo detection of the user is passed. According to the present invention, based on a cloud end, multiple first human face images of a user are consecutively collected; whether there is a micro action in the multiple consecutive first human face images is recognized after it is determined that each of the first human face images is an in vivo image; and if there is a micro action, it is confirmed that human face in vivo detection of the user is passed. By means of in vivo recognition and micro action recognition, human face in vivo detection is carried out on the user, thereby effectively improving the accuracy of human face in vivo detection, preventing the behavior of using a human face picture or a human face video to cheat a human face recognition system, realizing the function of distinguishing a real person from a dummy, and ensuring information security.

Description

Cloud-based human face detection method, electronic device and program product

Technical field

The present invention relates to the field of face detection technologies, and in particular, to a cloud-based method for detecting a living body of a living body, an electronic device, and a program product.

Background technique

With the development of deep learning technology, face has become a new type of authentication.

Compared with other biometric recognition technologies, face recognition technology can directly acquire the camera through the camera. It is convenient and fast, but it also brings some information security issues, such as face photos or face videos. Deceive the face recognition system.

Summary of the invention

The embodiment of the present application provides a cloud-based human face detection method, an electronic device, and a program product, which are mainly used for blind navigation.

In a first aspect, the embodiment of the present application provides a cloud-based method for detecting a living body of a human body, including:

Collecting a plurality of first face images of the user continuously;

After determining that each first face image is a living image, identifying whether there is a micro motion in the plurality of consecutive first face images;

If there is a micro motion, it is confirmed that the user's face is detected by the living body.

In a second aspect, an embodiment of the present application provides an electronic device, where the electronic device includes:

a memory, one or more processors; a memory coupled to the processor via a communication bus; a processor configured to execute instructions in the memory; the storage medium having stored therein for performing the steps of the method of the first aspect of the claims instruction.

In a third aspect, an embodiment of the present application provides a computer program product for use in conjunction with an electronic device including a display, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer The program mechanism includes instructions for performing the various steps in the method of the first aspect described above.

The benefits are as follows:

In the embodiment of the present application, a plurality of first face images of the user are continuously collected, and after each first face image is a living image, whether there is a micro motion in the plurality of consecutive first face images, if there is a micro The action confirms that the user's face is detected by the living body, and the living body detection is performed on the user through the living body recognition and the micro motion recognition, thereby effectively improving the accuracy of the face detection, and preventing the face recognition system from being passed through the face photo or the face video. The behavior of the real person to distinguish the function of the real person dummy, to ensure information security.

DRAWINGS

Specific embodiments of the present application will be described below with reference to the accompanying drawings, in which:

FIG. 1 is a schematic flowchart of a cloud-based method for detecting a human face in a cloud according to an embodiment of the present application;

2 is a schematic diagram of a key feature part of a face in the embodiment of the present application;

FIG. 3 is a schematic structural diagram of a deep neural network for micro-expression recognition according to an embodiment of the present application; FIG.

4 is a schematic flowchart of another cloud-based method for detecting a human face in the embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings, in which the embodiments described are only a part of the embodiments of the present application, but not all embodiments. An exhaustive example. And in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

At present, face recognition applications are more and more widely, but there is a core security problem in face recognition: face fraud, such as face recognition system can be deceived by face photo, face video or 3D face film.

In order to solve the above-mentioned face fraud problem, the security of the face recognition system is improved. The embodiment of the present application provides a cloud-based method for detecting a human face in vivo, which continuously collects a plurality of first face images of a user, and determines that each first face image is a living image, and identifies a plurality of consecutive first persons. Whether there is a micro-action in the face image. If there is a micro-action, it is confirmed that the user's face is detected by the living body, and the living body detection is performed on the user through the living body recognition and the micro-motion recognition, thereby effectively improving the accuracy of the face detection and preventing the person passing through. The face photo or the face video deceives the behavior of the face recognition system to realize the function of distinguishing the real person dummy and ensure the information security.

Referring to FIG. 1 , the cloud-based method for detecting a human face in vivo according to the embodiment includes:

101. Determine that the user meets the distance requirement.

Commonly used intrusion methods for face recognition are usually printed photos including face images or face videos/mobile screens/computer screens/3D masks, etc. These invasive tools usually have characteristic differences from normal living faces. In order to better identify the difference, this proposal first requires the distance between the user (such as the face) from the recognition device (such as the camera), and recognizes the difference between the features while keeping the camera and the face at an appropriate distance. .

Determine the implementation of the user to meet the distance requirements, including but not limited to:

Step 1: Acquire a second face image of the user.

The second face image is an image used by the user to adjust the distance, which is different from the image used for subsequent face recognition.

Step 2: Acquire a face area in the second face image.

In step 3, the user distance is determined according to the face area.

Specifically, the user distance may be determined according to the proportion of the face area occupying the second face image. It is also possible to extract the distance between the face preset parts from the face area, and determine the user distance according to the ratio of the distance to the width and height of the second face image.

Step 4: If the user distance matches the distance requirement, it is determined that the user meets the distance requirement.

Step 5: If the user distance does not match the distance requirement, the user is instructed to move to meet the distance requirement.

Specifically, a prompt (such as a voice prompt or a text prompt) can be sent to the user to guide the user to adjust their position, manner, and the like. After the adjustment, step 1 to step 3 are performed again to determine whether the adjusted distance matches the distance requirement. If it matches, step 4 is performed again; otherwise, step 5 is performed again. This cycle until the user meets the distance requirement.

For example, when the embodiment is executed, face detection is performed first, and a face area is obtained. The distance of the face can be approximated according to the size of the face area and the proportion of the area in the image. If it is within the proper specific gravity range, it is considered to be within the optimal distance, otherwise the user is approached or away according to the magnitude of the ratio. In addition, it is also possible to detect some key features (points) of the human face, as shown in Figure 2, according to the ratio of the distance between these key parts (points) and the width and height of the image to determine the distance, such as detecting first Both eyes are at a ratio of the distance from the center of the two eyes to the width of the image.

When calculating the user distance, the 2D coordinates of the key points can be obtained, and then the 3D pose Euler angle and the 3D translation (T _x , T _y , T _z ) of the face relative to the camera are obtained by the solvepnp algorithm, and the 3D distance is further obtained, and then judged. Is the distance within the proper range?

After judging whether the distance is within the appropriate range, the posture of the user's face may be reminded (such as roll, pitch, yaw) according to the detection result of the position and posture described above, and the position in the 2D image is reminded ( Left, right, upper, lower, etc.).

User reminder here: you can voice prompts, or you can prompt in the form of text on the image.

Among them, the roll is rotated around the Z axis, also called the roll angle. The pitch is rotated around the X axis, also known as the pitch angle. Yaw is rotated around the Y axis, also called the yaw angle.

102. Collect multiple first face images of the user continuously.

After confirming that the user meets the distance requirement, the user's continuous and multiple face images, that is, the first face image, are collected. Here, the first face image user performs the basis for the face detection of the user.

103. Determine whether each first face image is a living image.

For any first face image,

If it is determined that any of the first face images is a living image, any one of the first face images is stored in the image sequence.

The image sequence here is initially empty, and it is determined that a first face image is a living image, and the first face image is stored in the image sequence, and then whether the next face image of a certain frame is performed For the detection of the living image, if the next picture of a certain picture is a living body image, the next picture of a certain picture is stored in the image sequence, and the loop is repeated until all the first face images are subjected to the living body image detection. If a next non-living image of a certain piece is found during the detection, the face image in the image sequence is cleared at this time.

If it is determined that any one of the first face images is not a living image, the process is terminated, the image sequence is cleared, and the face detection of the user does not pass.

Among them, non-living images, including but not limited to: photos (such as print photos, photos on the phone screen, photos on the computer screen), videos (such as video on the phone screen, video on the computer screen), facial mask (such as 3D face mask).

Filtering of a single image can be achieved by step 103.

Specifically, the single image is classified and discriminated by a method of machine learning.

For example, CNN (Convolutional Neural Network) based on deep learning is used for classification and discrimination, such as using a very popular resnet classification network for classification and discrimination.

First, collect various possible fraud samples and train them. For example, you can divide into several categories: print photo/mobile screen/computer screen/3D face/normal face for training.

After CNN training is completed, each first face image is classified and identified by using the successfully trained network model and weight. Which type of output probability is large, that is, which type can be considered, and the threshold can be set for further discrimination, for example. The maximum probability is greater than a set value.

If the classification result is a normal face, step 104 is performed to perform image sequence classification discrimination. If the classification results in other categories, the image sequence is cleared and the entire inspection process is restarted.

104: Identify whether there are micro actions in the plurality of consecutive first face images.

If there is a micro action, step 105 is performed.

If there is no micro action, the process is terminated, the image sequence is cleared, and the user's face biometric detection does not pass.

After the execution 103, the recognition of the print photo/mobile phone screen/computer screen/3D face film including the face image or the face video can be realized, but only the recognition result is determined to determine whether the user face biometric detection still exists. The case of misjudgment.

In the whole process of face recognition, people will make many inadvertent micro-actions, such as slight changes in the eyes and mouth, or deformation of the facial muscles, or slight shaking of the head, through the micro-action The identification can further improve the accuracy of the human face detection.

Specifically, the image sequence classification filtering is performed through step 104.

For example, when the length of the image sequence satisfies a certain length, image sequence classification filtering is performed. The image sequence is input into a deep neural network for classification and discrimination, and the output is two categories: normal face and abnormal face.

The deep neural network can be directly based on the 3D convolutional neural network, or a general 2D convolutional neural network, such as resnet, except that the network input is the stacked sequence image data, as shown in FIG.

The general resnet classification network input is 1 channel or 3 channels. After the image sequence is stacked, the color image is taken as an example, which is equivalent to inputting N*3 channel data.

Where N is the length of the image sequence of the input depth neural network, ie the number of first face images in the image sequence of the input depth neural network.

For example, if the image sequence is input to the depth neural network for classification and discrimination, N is the number of all the first face images in the image sequence.

Then, according to the two types of samples collected, the 3D convolutional neural network or the 2D convolutional neural network can be trained. After the training is completed, the input image sequence is directly discriminated using the trained model and weight. Which category has a large output probability, which is the same type, and the threshold can also be set for further filtering.

For the case where the image sequence is directly input into the deep neural network for classification and discrimination, if the final output is a normal face, it is determined that there is a micro-action, and the step 105 is performed to perform the bio-detection, otherwise it is determined that there is no micro-action, the process is terminated, and the image sequence is cleared. The user's face is not detected by the living body, and the entire detection process is restarted.

105. Confirm that the user's face is detected by the living body.

So far, the cloud-based human face detection method of the present embodiment is completed.

Referring to the flow shown in FIG. 4, the cloud-based human face detection method of the present embodiment will be described again.

Firstly, the face distance detection is performed to remind the user and the camera to maintain a suitable distance for subsequent living body detection; then, a single face image is collected for classification, and it is judged to be a print photo/mobile screen/computer screen/3D face/normal face. Filtering abnormal faces; finally, sorting the sequence of consecutive pictures filtered by face to determine whether it is a real person.

Beneficial effects:

In the embodiment of the present application, a plurality of first face images of the user are continuously collected, and after each first face image is a living body image, whether there are micro motions in the plurality of consecutive first face images, if there is a micro action , to confirm that the user's face is detected by the living body, and the living body detection is performed on the user through the living body recognition and the micro motion recognition, thereby effectively improving the accuracy of the face detection, and preventing the face recognition system from being deceived by the face photo or the face video. Behavior, to achieve the function of distinguishing between real people and dummy, to ensure information security.

Based on the same concept, the embodiment of the present application further provides an electronic device. Referring to FIG. 5, the electronic device includes:

a memory 501, one or more processors 502; and a transceiver component 503, the memory, the processor, and the transceiver component 503 are connected by a communication bus (in the embodiment of the present application, the communication bus is an I/O bus); The storage medium stores instructions for performing the following steps:

Collecting a plurality of first face images of the user continuously;

After determining that each first face image is a living image, identifying whether there are micro actions in the plurality of consecutive first face images;

If there is a micro-action, it is confirmed that the user's face is detected by the living body.

Optionally, before continuously acquiring the plurality of first face images, the method further includes:

Make sure the user meets the distance requirement.

Optionally, determining that the user meets the distance requirement includes:

Obtaining a second face image of the user;

Obtaining a face area in the second face image;

Determining the user distance according to the face area;

If the user distance matches the distance requirement, it is determined that the user meets the distance requirement.

Optionally, determining the user distance according to the face area, including:

Determining the user distance according to the proportion of the face area occupying the second face image; or

The distance between the face preset parts is extracted from the face area, and the user distance is determined according to the ratio of the distance to the width and height of the second face image.

Optionally, after determining the user distance according to the face area, the method further includes:

If the user distance does not match the distance requirement, the user is instructed to move to meet the distance requirement.

Optionally, after continuously collecting the plurality of first face images of the user, the method further includes:

Determining whether each first face image is a living image;

For any first face image, if any one of the first face images is determined to be a living image, any one of the first face images is stored in the image sequence; if any one of the first face images is determined If the image is not a live image, the process is terminated, the image sequence is cleared, and the user's face is not detected.

Optionally, the non-living image includes: a photo, a video, a face film.

Optionally, the micro-motions include micro-changes in the face organs, slight changes in the face muscles, and micro-movement of the faces.

Optionally, after identifying whether there are micro actions in the plurality of consecutive first face images, the method further includes:

If there is no micro motion, the process is terminated, and the first face image in the image sequence is cleared, and the user's face biometric detection does not pass.

It is not difficult to understand that, in the specific implementation, in order to achieve the basic purpose of the present application, the above-mentioned transceiver component 503 is not necessarily required.

Beneficial effects:

In still another aspect, an embodiment of the present application further provides a computer program product for use in conjunction with an electronic device including a display, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, The computer program mechanism includes instructions for performing the various steps described below:

Collecting a plurality of first face images of the user continuously;

Make sure the user meets the distance requirement.

Optionally, determining that the user meets the distance requirement includes:

Obtaining a second face image of the user;

Obtaining a face area in the second face image;

Determining the user distance according to the face area;

Determining whether each first face image is a living image;

Optionally, the non-living image includes: a photo, a video, a face film.

Beneficial effects:

Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, system, or computer program product. Thus, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware. Moreover, the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

While the preferred embodiment of the present application has been described, it will be apparent that those skilled in the art can make further changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and the modifications and

Claims

A cloud-based method for detecting a living body in a living body, comprising:

Collecting a plurality of first face images of the user continuously;

After determining that each first face image is a living image, identifying whether there is a micro motion in the plurality of consecutive first face images;

If there is a micro motion, it is confirmed that the user's face is detected by the living body.
The method according to claim 1, wherein before the collecting the plurality of first face images in succession, the method further comprises:

Make sure the user meets the distance requirement.
The method of claim 2, wherein the determining that the user meets the distance requirement comprises:

Obtaining a second face image of the user;

Obtaining a face region in the second face image;

Determining the user distance according to the face area;

If the user distance matches the distance requirement, it is determined that the user satisfies the distance requirement.
The method according to claim 3, wherein the determining the user distance according to the face area comprises:

Determining the user distance according to the proportion of the face area occupying the second face image; or

Extracting a distance between the face preset portions from the face region, and determining the user distance according to a ratio of the distance to a width and height of the second face image.
The method according to claim 4, wherein after determining the user distance according to the face area, the method further comprises:

If the user distance does not match the distance requirement, the user is instructed to move to meet the distance requirement.
The method according to any one of claims 1 to 5, wherein after the plurality of first face images of the user are continuously collected, the method further comprises:

Determining whether each first face image is a living image;

For any one of the first face images, if it is determined that any one of the first face images is a living image, the one of the first face images is stored in the image sequence; If the first face image is not a live image, the process is terminated, the sequence of the image is cleared, and the user's face is not detected.
The method according to claim 6, wherein the non-living image comprises: a photo, a video, a face film.
The method according to any one of claims 1 to 7, wherein the micro-motions include micro-changes in facial organs, slight changes in facial muscles, and micro-movement of human faces.
The method according to any one of claims 1 to 8, wherein after the identifying the presence of the micro-action in the plurality of consecutive first face images, the method further comprises:

If there is no micro action, the process is terminated, the image sequence is cleared, and the user's face biometric detection fails.
An electronic device, comprising:

a memory, one or more processors; a memory coupled to the processor via a communication bus; a processor configured to execute instructions in the memory; the storage medium having stored therein for performing the method of any of claims 1-9 Instructions for each step.
A computer program product for use in conjunction with an electronic device including a display, the computer program product comprising a computer readable storage medium and a computer program mechanism embodied therein, the computer program mechanism comprising for performing claim 1 9 The instructions of the various steps in any of the methods described.