Face Spoof Detection Using VGG-Face Architecture

Abstract

Face recognition systems have been obtaining substantial importance in modern world. Security systems are major application of face recognition system. However, the potential of the face recognition system to withstand the attack of an unauthorized person is an important concern. Face recognition systems are vulnerable to photographs and video spoof attacks. In these scenarios, anti-spoofing systems comes in handy to evade these attacks. Robust solutions are required for face recognition system to be immune against spoofing attacks. In this paper, the detected face is denoised and then converted to YCbCr and CIELUV colour model and then passed through VGG-Face architecture for extraction of face embeddings of each colour space. Then the extracted face embeddings are concatenated and then passed through SVC (Support Vector Classifier) which then classifies real and spoof faces. The proposed method has obtained a test accuracy of 99.6% with specificity of 99.5% for spoof detection.