Face anti-fraud method and system based on asymmetric auxiliary information embedded network

Abstract

The invention provides a face anti-fraud method and system based on an asymmetric auxiliary information embedded network. The method comprises the steps: obtaining a face image, and obtaining the positions of face key points; performing preprocessing operation on the face image according to the face key point position; inputting the face image subjected to the preprocessing operation into a pre-trained asymmetric auxiliary information embedding network, and obtaining a confidence coefficient list which corresponds to the face image and is judged as a real face; and comparing the values in the confidence coefficient list with a preset confidence coefficient threshold value to obtain anti-fraud detection results of all faces in the face image. According to the scheme, through an asymmetric multi-classification method and asymmetric triple loss, on one hand, the algorithm is guided to find features of multiple attack types, and on the other hand, the distance between a real face category and other multiple false face attack categories is increased, so that the generalization of the algorithm to the attack types and the practicability in practical application are improved.

Description

technical field [0001] The disclosure belongs to the technical fields of artificial intelligence, pattern recognition, and digital image processing, and in particular relates to a face anti-fraud method and system based on embedding asymmetric auxiliary information into a network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Face anti-spoofing, also known as liveness detection, face expression attack detection, etc., aims to judge whether the captured face is a real face or a fake face attack (such as: paper printed image, image or video on an electronic screen, masks, etc.). Commonly used face representation attack tools include paper, electronic screens, and 3D masks. [0004] In recent years, with the development of artificial intelligence and deep learning, face recognition technology has been widely used in our daily life, such ...

AI Technical Summary

Problems solved by technology

The inventors discovered that the human face is easier to obtain than other biometrics such as fingerprints and irises, but in a malicious environment, this advantage becomes a weakness, and an attacker can easily spoof face recognition using a photo or video of a valid user system

On the other hand, with the development of science and technology, there are more and more complex attack tools such as 3D masks, which make the face recognition system face greater threats; in recent years, face anti-fraud technology has made some progress, but Existing works still have difficulties in dealing with complex spoofing attacks and applying them to real scenarios

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