Face spoofing attack detection based on the behavior of noises
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Institute of Electrical and Electronics Engineers Inc.
This paper aims to study the problem of spoofing attack detection for facial recognition systems. Real faces and falsified faces present in front of a security system (phone's camera in our case) have differences of micro-textures on their surface, which are exploited to discriminate face spoofing images. Our method exploits the statistic behavior of the distribution of noise's local variances, which performs differently between images of real faces and the fake ones. We test our method on two databases constructed in our laboratory. We used SVM for classification method. Experimental results show that the proposed method has an encouraging performance. © 2016 IEEE.