Open AccessFace Spoof Detection Using VGG-Face Architecture
Author(s) -
K Balamurali,
S. Chandru,
Muhammed Sohail Razvi,
Vikas Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1917/1/012010
Subject(s) - computer science , artificial intelligence , face (sociological concept) , computer vision , facial recognition system , ycbcr , face detection , spoofing attack , object class detection , classifier (uml) , architecture , pattern recognition (psychology) , computer security , image (mathematics) , image processing , art , social science , visual arts , sociology , color image
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.