Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security

Abstract

Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet-5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed model has a high recognition rate in face liveness detection.