Open AccessFace Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks
Author(s) -
JesslinMelba N V*,
U. Poornima,
J Blessy
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8876.018520
Subject(s) - computer science , spoofing attack , local binary patterns , artificial intelligence , convolutional neural network , deep learning , biometrics , feature extraction , histogram of oriented gradients , pattern recognition (psychology) , histogram , face (sociological concept) , frame (networking) , facial recognition system , computer vision , image (mathematics) , computer security , telecommunications , social science , sociology
Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.