CN119107700A - A face recognition method, system and device based on silent living body detection algorithm - Google Patents
A face recognition method, system and device based on silent living body detection algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of face recognition and discloses a face recognition method, a face recognition system and a face recognition device based on a silent living body detection algorithm, wherein the technical scheme of the face recognition method is characterized by comprising the following steps that S1, an image acquisition device is started to acquire a target area video image in response to face recognition requirements; S2, face position detection is carried out on the target area video image, the face position is marked in the target area video image, the face image is obtained, S3, feature extraction is carried out on the face image, face feature vectors are obtained, S4, living body recognition is carried out on the face feature vectors through a silent living body detection algorithm, S5, face storage or face recognition detection is carried out according to face recognition requirements after the fact that the real face exists in the target area is obtained, and recognition results are obtained.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a face recognition method, a face recognition system and a face recognition device based on a silent living body detection algorithm.
Background
In recent years, face recognition technology has been gradually penetrated in many aspects of our lives, such as widely applied to the fields of finance, security anti-terrorism, education, social entertainment, equipment, entrance guard/attendance, traffic, intelligent business and the like. Face payment, face arrival, face pickup, face passing and other face recognition modes bring convenient and intelligent experience to our lives.
However, in most of the existing face recognition processes, people need to make corresponding actions to better recognize, but in the recognition mode, people need to cooperate, and practical use is inconvenient, so that silence living body detection occurs.
The silence living body detection means that the user does not need to do any action and can naturally face the camera for 3 and 4 seconds. Since the real face is not absolutely stationary, there are micro-expressions such as rhythms of eyelid eyeballs, blinks, stretching of lips and surrounding cheeks, etc., which can be spoofed by such features.
The silence living body detection algorithm is a technology based on a machine learning algorithm, captures face images through an RGB camera, and can quickly judge whether the face is a real face or not without the need of a user to cooperatively make specific actions such as mouth opening, head twisting, blink and the like.
The core of the silence living body detection technology is that whether a detected object in front of a camera is a real face can be identified and analyzed, and the real face and false face attack can be distinguished by analyzing detailed information such as mole marks, paper photo reflection and the like which appear in screen shooting. The technology covers a wide range of application scenarios including, but not limited to, mobile phone unlocking, banking transaction, social media registration and other scenarios requiring real-name authentication. By analyzing the macroscopic environment of the human face, such as illumination, face ornaments, gender, hair style, mask materials and the like, the silence living body detection technology can effectively eliminate the scene that the human face is obviously impossible to exist, thereby avoiding serious losses of personal information leakage and the like caused by the attack of the false face.
In technical implementation, silence living detection algorithms improve their accuracy and efficiency by a variety of methods. For example, the generalized feature space expression is learned by training under different data sets to obtain models of respective fields, then extracting features using the models, training the feature generator and the discriminators of the fields until the features output by the generator can successfully spoof the discriminators of the respective fields. In addition, these methods all aim to improve the performance and accuracy of the silence in-vivo detection algorithm by designing inter-domain triplets, i.e., for each subject it is desired that its positive distance across the domain is less than its negative distance across the domain, and adding the task of predicting depth to enhance discriminance.
Care should be taken to preserve user privacy to ensure that user individuals are collected, used and stored when using silent living detection techniques
In summary, the silence living body algorithm is an important security technology, and provides a safer and more convenient identity verification mode for users through advanced machine learning algorithms and continuous algorithm updating. Therefore, it is necessary to continuously upgrade and optimize the existing silence living body algorithm, so that it obtains higher accuracy and convenience in the actual face recognition process.
Disclosure of Invention
The invention aims to provide a face recognition method, a face recognition system and a face recognition device based on a silent living body detection algorithm, which effectively recognize a real face through extracting and analyzing various face features and a living body detection technology so as to prevent financial fraud and identity impersonation and improve the safety and reliability of a financial system.
The technical aim of the invention is achieved by the following technical scheme that the face recognition method based on the silent living body detection algorithm comprises the following steps:
S1, responding to a face recognition requirement, starting an image acquisition device, and acquiring a video image of a target area;
s2, detecting the face position of the target area video image, and marking the face position in the target area video image to obtain a face image;
S3, extracting features of the face image to obtain a face feature vector;
s4, performing living body recognition on the face feature vector through a silent living body detection algorithm;
And S5, after the fact that the real human face exists in the target area is obtained, human face storage or human face recognition detection is carried out according to human face recognition requirements, and a recognition result is obtained.
As an optimal technical scheme, the image acquisition device is a high-resolution camera, and the high-resolution camera has a self-adaptive dimming function and an automatic focusing function.
As a preferable technical scheme of the invention, the duration of the target area video image is at least t1, and the target area video image at least comprises n image frames, wherein n is greater than 2.
In a preferred technical scheme of the invention, in S2, before face detection, optimization pretreatment is carried out on the target area video image, wherein the optimization pretreatment comprises the steps of adjusting the target area video image to a preset image size and carrying out graying treatment on the target area video image.
In S2, a face detection algorithm is adopted to perform face recognition on the target area image, determine the face position, and extract the face image of the face area alone.
In a preferred technical scheme of the invention, in S3, the face feature vector is extracted from the face image and comprises a face static feature vector, a face dynamic feature vector and a face depth feature vector.
As a preferred embodiment of the present invention, in S4, the living body recognition includes:
According to the face static feature vector, optical reflection analysis and skin texture analysis are carried out;
detecting the micro expression according to the dynamic feature vector of the face;
carrying out face depth structure analysis according to the face depth feature vector;
and when the three vector analyses all accord with the real face information data, the face in the current face image is considered to be the real face.
In S5, the face recognition requirement includes an acquisition class and a verification class;
If the face feature vector is the acquisition type, the face feature vector is stored in a face feature database, and the success of face acquisition is prompted;
if the face feature vector is of a verification type, carrying out normalization processing on the face feature vector to obtain a standardized face feature vector;
And inputting the standardized face feature vector into an intelligent classifier to obtain a judgment result.
The intelligent classifier is a classifier trained and tested by the labeled real face and photo data set.
A face recognition system based on a silence living-body algorithm, comprising:
the image acquisition module is used for responding to the face recognition requirement, starting the image acquisition device and acquiring the video image of the target area;
The face positioning module is used for detecting the face position of the target area video image, marking the face position in the target area video image and obtaining a face image;
The feature extraction module is used for extracting features of the face image to obtain a face feature vector;
The living body recognition module is used for carrying out living body recognition on the face feature vector through a silence living body algorithm;
And the face recognition module is used for carrying out face preservation or face recognition detection according to the face recognition requirement after obtaining that the real face exists in the target area, so as to obtain a recognition result.
A face recognition device based on a silent living body detection algorithm comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor realizes the method when executing the computer program.
In summary, the invention has the advantages that the real face can be accurately and efficiently identified by extracting and analyzing various face features and a living body detection technology, so as to prevent financial fraud and identity impersonation and improve the safety and reliability of a financial system. And unlike the traditional user interaction verification, the face recognition method does not need the active cooperation of the user, can realize living body detection by analyzing the image captured by the camera, is more convenient for the user to use, and can improve the use experience of the user.
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Fig. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a face recognition method and system based on a silent living body detection algorithm, wherein the method is performed by each module in the system, and comprises the following steps:
s1, an image acquisition module responds to a face recognition requirement, and an image acquisition device is started to acquire a video image of a target area;
The image acquisition device is a high-resolution camera, and the acquired image can have higher definition, so that the face detail image of the user can be captured more clearly;
The high-resolution camera has a self-adaptive dimming function and an automatic focusing function. The self-adaptive dimming function can ensure that face images of users can be clearly acquired under the condition of different light rays, and avoid more negative interference to the imaging of the users caused by the factors of surrounding environment light rays, thereby expanding the practical application scene. The automatic focusing function can automatically find the face of the user, so that the focal length is matched with the position of the user, the final imaging can be clearer, and more face details of the user are obtained.
The duration of the target area video image is at least t1, generally t1 is more than two seconds, during which effective micro-motions of a user can be captured, the target area video image at least comprises n image frames, and n is more than 2, so that a plurality of image frames exist for mutual comparison, the change of micro-expressions of the user is obtained, and the actual face recognition is facilitated.
S2, a face positioning module detects the face position of the target area video image, marks the face position in the target area video image and obtains a face image;
In S2, before face detection, optimizing and preprocessing the target area video image, wherein the optimizing and preprocessing comprises the steps of adjusting the target area video image to a preset image size and carrying out graying processing on the target area video image.
And S2, carrying out face recognition on the target area image by adopting a face detection algorithm, determining the face position, and independently extracting the face image of the face area. The face detection algorithm may employ a Haar feature cascade classifier or a deep learning model (e.g., MTCNN).
S3, a feature extraction module performs feature extraction on the face image to obtain a face feature vector;
And S3, extracting face feature vectors from the face image, wherein the face feature vectors comprise face static feature vectors, face dynamic feature vectors and face depth feature vectors. The face static features comprise face geometric structures, texture features and the like, the dynamic features comprise micro-expressions of various faces, such as eye blinking, mouth opening and closing, facial muscle actions and the like, and the depth features are depth of face structures and express three-dimensional structure data of the faces.
S4, a living body recognition module carries out living body recognition on the face feature vector through a silent living body detection algorithm;
In S4, the living body recognition includes:
according to the static feature vector of the human face, optical reflection analysis and skin texture analysis are carried out, so that whether the human face is a real human face or a picture or a video is analyzed;
According to the dynamic feature vector of the human face, the micro-expression detection is carried out, and as the real human face does not keep static, a certain micro-expression exists, the method can be used for judging whether the real human face is the real human face or not;
According to the depth feature vector of the human face, the human face depth structure analysis is carried out, and as the human face is of a three-dimensional structure, whether the human face is of a three-dimensional structure or not can be judged through the depth analysis, if not, the human face is not the real human face, and the human face depth structure analysis can be realized through the function of the image acquisition device, can also optimize a camera, and can obtain depth information more quickly by using the depth camera or an infrared camera.
And when the three vector analyses all accord with the real face information data, the face in the current face image is considered to be the real face.
S5, after the real face exists in the target area, the face recognition module stores the face or performs face recognition detection according to the face recognition requirement to obtain a recognition result.
S5, the face recognition requirement comprises acquisition class and verification class;
If the face feature vector is the acquisition type, the face feature vector is stored in a face feature database, and the success of face acquisition is prompted;
if the face feature vector is of a verification type, carrying out normalization processing on the face feature vector to obtain a standardized face feature vector;
And inputting the standardized face feature vector into an intelligent classifier to obtain a judgment result.
The intelligent classifier is a classifier trained and tested by the labeled real face and photo data set. When in actual use, the judgment result can be automatically obtained only by inputting the extracted adjustment vector into the intelligent classifier in real time.
As an embodiment of the invention, the method of the invention also evaluates the performance of the method under different light rays, angles and backgrounds by verifying the accuracy and the robustness of the algorithm through experiments before application. The method comprises the steps of data set construction, namely collecting a large number of real human faces and photo samples, and constructing training and testing data sets. Performance indexes such as accuracy, recall rate, F1 value, false positive rate, false negative rate and the like. And (3) analyzing experimental results, namely comparing the performances of different feature extraction methods and classifiers, and optimizing algorithm parameters. Therefore, the method has more reliable application effect.
Corresponding to the method, the face recognition device based on the silence living body detection algorithm comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor realizes the methods of S1-S5 when executing the computer program.
The face recognition method, the face recognition system and the face recognition device based on the silent living body detection algorithm have the advantages that the real face is effectively recognized through extraction and analysis of various face features and living body detection technology, so that financial fraud and identity impersonation are prevented, and the safety and the reliability of a financial system are improved. And unlike the traditional user interaction verification, the face recognition method does not need the active cooperation of the user, can realize living body detection by analyzing the image captured by the camera, is more convenient for the user to use, and can improve the use experience of the user.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (10)
1. A face recognition method based on a silent living body detection algorithm is characterized by comprising the following steps:
S1, responding to a face recognition requirement, starting an image acquisition device, and acquiring a video image of a target area;
s2, detecting the face position of the target area video image, and marking the face position in the target area video image to obtain a face image;
S3, extracting features of the face image to obtain a face feature vector;
s4, performing living body recognition on the face feature vector through a silent living body detection algorithm;
And S5, after the fact that the real human face exists in the target area is obtained, human face storage or human face recognition detection is carried out according to human face recognition requirements, and a recognition result is obtained.
2. The face recognition method based on the silence living body detection algorithm according to claim 1 is characterized in that the image acquisition device is a high-resolution camera, and the high-resolution camera has an adaptive dimming function and an automatic focusing function.
3. The method for recognizing human face based on the silence living body detection algorithm according to claim 2, wherein the duration of the target area video image is at least t1, and the target area video image comprises at least n image frames, wherein n >2.
4. The face recognition method based on the silence living body detection algorithm of claim 3, wherein in S2, before face detection, the target region video image is subjected to optimization preprocessing, the optimization preprocessing comprises the steps of adjusting the target region video image to a preset image size, and carrying out graying processing on the target region video image.
5. The face recognition method based on the silence living body detection algorithm of claim 4, wherein in S2, face recognition is performed on the target area image by adopting a face detection algorithm, the face position is determined, and the face image of the face area is extracted independently.
6. The face recognition method based on the silence living body detection algorithm of claim 5, wherein in S3, the face feature vector is extracted from the face image and comprises a face static feature vector, a face dynamic feature vector and a face depth feature vector.
7. The face recognition method based on the silent living body detection algorithm as claimed in claim 6, wherein in S4, the living body recognition comprises:
According to the face static feature vector, optical reflection analysis and skin texture analysis are carried out;
detecting the micro expression according to the dynamic feature vector of the face;
carrying out face depth structure analysis according to the face depth feature vector;
and when the three vector analyses all accord with the real face information data, the face in the current face image is considered to be the real face.
8. The face recognition method based on the silent living body detection algorithm of claim 7, wherein in S5, the face recognition requirement comprises acquisition class and verification class;
If the face feature vector is the acquisition type, the face feature vector is stored in a face feature database, and the success of face acquisition is prompted;
if the face feature vector is of a verification type, carrying out normalization processing on the face feature vector to obtain a standardized face feature vector;
And inputting the standardized face feature vector into an intelligent classifier to obtain a judgment result.
The intelligent classifier is a classifier trained and tested by the labeled real face and photo data set.
9. A face recognition system based on a silence living body detection algorithm is characterized by comprising:
the image acquisition module is used for responding to the face recognition requirement, starting the image acquisition device and acquiring the video image of the target area;
The face positioning module is used for detecting the face position of the target area video image, marking the face position in the target area video image and obtaining a face image;
The feature extraction module is used for extracting features of the face image to obtain a face feature vector;
The living body recognition module is used for carrying out living body recognition on the face feature vector through a silence living body algorithm;
And the face recognition module is used for carrying out face preservation or face recognition detection according to the face recognition requirement after obtaining that the real face exists in the target area, so as to obtain a recognition result.
10. A face recognition device based on a silent living body detection algorithm is characterized by comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor realizes the method of any one of claims 1-8 when executing the computer program.
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