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

Yang Wei, Ivy Kim D. Machica, Cristina E. Dumdumaya, Jan Carlo T. Arroyo, AllemarJhone P. Delima

2022International Journal of Emerging Technology and Advanced Engineering20 citationsDOIOpen Access PDF

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 LeNet5 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

Topics & Concepts

LivenessBiometricsConvolutional neural networkComputer sciencePoolingArtificial intelligenceFace (sociological concept)Pattern recognition (psychology)Support vector machineFacial recognition systemFace Recognition Grand ChallengeConvolution (computer science)Speech recognitionFace detectionArtificial neural networkTheoretical computer scienceSocial scienceSociologyBiometric Identification and SecurityFace recognition and analysisFace and Expression Recognition
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