Face recognition method and device

Abstract

The present disclosure relates to a face recognition method and device. The method includes acquiring a first image and a second image including a face of an object to be tested, and the imaging methods of the first image and the second image are different; extracting first feature information from the first image; Extracting the second characteristic information from the image; performing data fusion on the first characteristic information and the second characteristic information; performing optical flow processing on the characteristic information after data fusion; determining the object to be measured according to the characteristic information processed by the optical flow method Is it alive. In this disclosure, the characteristics of images obtained by different imaging methods are fused, and then processed by the streamer method, so as to determine whether the object to be tested is a living body, thereby preventing the use of photo or video replay deception, and improving the anti-aggression of face recognition ability. In addition, using each feature part of the face to weight the feature can further improve the recognition speed, anti-aggression ability and light adaptation ability of infrared face recognition.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular to a face recognition method and device. Background technique [0002] Face recognition is a very hot research field in biometric authentication. Face recognition is favored by people for its advantages of natural interactive interface, cheap acquisition equipment, non-contact acquisition, and ease of use. It can be applied to access control attendance, system login, entry-exit management, suspect photo comparison, etc. However, there are not many researches on the detection of face forgery attacks, which restricts the application of face authentication systems. At the same time, the diversification of face spoofing methods also brings great challenges to the research of face spoofing attack detection technology, so the research on face spoofing attack detection technology has increasingly become a hot spot in the field of biometric authentication research. ...

AI Technical Summary

Problems solved by technology

[0007] 2. Poor security

At present, the infrared face recognition system has certain advantages for static images, but it is vulnerable to external video or image replay, 3D model attacks

For example, experiments by Kollreider et al. showed that the optical flow method can effectively detect photo spoofing, but cannot detect video spoofing.

This method also requires the user's head to make a little movement, and because the method estimates the movement of different positions of the head through optical flow, the change of light will seriously interfere with the method's estimation of the movement of different parts

[0008] 3. Weak adaptability

Part of the face recognition method is based on image quality and reflectivity, so the image has high requirements on the lighting of the application scene, which limits the application of face recognition

For example, the high-frequency descriptor proposed by J. Li et al. is mainly based on the fact that the clarity of the face will generally decrease after the photo is imaged. Therefore, if a high-resolution photo of a large face is used to deceive, this method cannot be detected.

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