Research On Liveness Detection Technology In Face Recognition System

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,It can not be ignored that the face recognition system is highly vulnerable to attacks by illegal users,the existing face recognition system can not recognize the legal users and illegal users on the market recently.As a result,the technology of face anti-spoofing becomes an important topic research topic in computer vision.Therefore,this paper proposes two improved face liveness detection methods and optimizes the traditional face detection approach.The main research contents are as follows:1.Before face liveness detection,face detection is conducted,however,traditional SMQT-SNOW face detection method can not detect face precisely.This paper adds face alignment algorithm of 3000 fps into the SMQT-SNOW face detection method.3000 fps algorithm locates the 68 feature points of the face to optimize the face bounding box,finally gets more accurate face detection.2.The paper proposes the improved face anti-spoofing approach based on the micro texture features.The algorithm decomposes face images by four-level Haar wavelet decomposition,calculates the means,variances of high frequency sub-bands coefficients matrixes which are obtained from one-level to four-level Haar wavelet decomposition,and extracts ULBP histogram,then cascades them to act as final 75 dimensional feature vectors to train SVM classifier to determine whether the given face is genuine or not.3.The paper proposes a novel face anti-spoofing approach based on improved parallel convolutional neural network(P-CNN).From the angle of deep learning,P-CNN is designed,and the ELM method is first utilized for face liveness detection.The P-CNN network includes an improved eight-layer model and an improved six-layer model respectively.The input image of the two models are not same to learn comprehensive features,then PCA is used to reduce dimensions.The models both have alternating sampling layer using mean sampling,random sampling and overlapping sampling,and utilize the dropout regularization to prevent overfitting.Experimen verifications about the two approaches are conducted on the two public face databases,e.t.NUAA and REPLAY-ATTACK,the average accuracy rates of the method based on micro-texture are 99.53% and 92.95% respectively,and the average accuracy rates of the method based on P-CNN are 99.96% and 97.14% respectively.Meanwhile pictures about ROC curves show the effectiveness about the proposed methods.The advantage of the first method is that the computational cost is low,the latter's is the high detection accuracy rate,and ELM acquires the fast classification speed.It provides technological foundation to the future researchers and reference in commercial face recognition system containing face liveness detection technology.