Face anti-counterfeiting method based on face depth information and edge image fusion

Abstract

The invention provides a face anti-counterfeiting method based on face depth information and edge image fusion, and the method comprises the steps: respectively extracting the edge information and depth image information of a face image through a double-flow network, carrying out the fusion of two types of features, and then carrying out the learning and classification through a feature fusion classification network, wherein a Sobel operator is used for extracting edge information of a face image, a PRNe is used for acquiring three-dimensional structure information of a face of a preprocessedliving body object, and adopting a Z-Buffer algorithm for projection to obtain corresponding living body face depth label. Depth information extraction network branches in the double-flow network extract differentiated depth information of living and non-living faces, and a weighting matrix and an entropy loss supervision mode are adopted to enhance the depth discrimination between a face area anda background area. Compared with the prior art, the method is slightly influenced by factors such as image quality and illumination, the problem that the hardware depth information extraction cost ishigh is solved, the characteristics of background information are expanded, and learning of redundant noise is weakened.

Description

technical field [0001] The invention relates to a living body detection technology, in particular to a face anti-counterfeiting technology based on face depth information and edge image fusion. Background technique [0002] As the application fields of face recognition technology become more and more extensive, various challenges related to it are gradually emerging, and the security of the identity recognition system based on face biometrics has aroused widespread public concern. When a malicious attacker uses the facial information of a legitimate user to impersonate the user's identity in the form of photos or videos, the system may misidentify the attacking user and judge it as legitimate, which will pose a serious threat to identity security. In the face recognition system, face anti-counterfeiting technology is the guarantee of system security. At present, the method research on face anti-counterfeiting technology can be roughly divided into two categories: methods ba...

AI Technical Summary

Problems solved by technology

However, traditional feature methods are easily affected by lighting and image quality, and the parts involving interactive discrimination are also easily seen by replay attacks.

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