Cheating-prevention method in face recognition system

Abstract

The present invention discloses a cheating-prevention method in a face recognition system. The method comprises the steps of 1 obtaining a face gray-scale image; 2 carrying out the equivalent characteristic coding on the pixel points of the face gray-scale image obtained in the step 1 and then obtaining 59 dimensions of ULBP characteristic vectors by the histogram statistic; 3 carrying out the four-level Haar wavelet decomposition on the face gray-scale image; 4 splicing the characteristic vectors and then sending to a trained support vector machine (SVM) classifier, and predicting a label via a decision function; 5 collecting a set of positive and negative face samples to train and test the SVM classifier capable of discriminating the face cheating; 6 training the SVM, and then utilizing a test set in the step 5 to test three trained SVM, thereby selecting the SVM of a kernel function of the best performance to discriminate the true and false face images. Compared with the prior art, the greatest advantages of the present invention are that the cheating-prevention method in the face recognition system is small in calculation complexity, saves the time and space consumption, has an excellent face cheating-prevention performance, and can be used to guarantee the safety of the face recognition system.

Description

technical field [0001] The invention belongs to the technical field of face recognition, in particular to an anti-spoofing technology in face recognition. Background technique [0002] At present, China Merchants Bank's Shenzhen head office has introduced the "face-swiping withdrawal" ATM machine for the first time, without the need to insert a card to withdraw the card. However, "swiping face" is only an auxiliary verification method, and the cooperation of mobile phone number and withdrawal password is required to complete the business. This is because the face recognition system in the actual environment, such as access control, customs security, etc., is extremely vulnerable to false attacks by illegal users, mainly including four types of deception: photo face, screen display face, face video and 3D face model. In the face recognition system, the research on face anti-spoofing technology is particularly important. [0003] In recent years, many research institutions ...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the problem that the algorithm complexity of the current anti-spoofing technology is usually relatively large, the present invention proposes an anti-spoofing method in a face recognition system, using the method of equivalent local binary mode and Haar wavelet decomposition to extract the human face Image Microtexture Features Training SVM Classifier to Determine True and False Face Images

Images