Face spoofing detection using noise-based random feature and Fisher vector encoding

With the vast application of face recognition technology, its security risks have increased as systems are vulnerable to spoofing attacks with falsified faces, attracting many researchers' attention. In this paper, we proposed to make use of noise information in colour space to detect spoofing attacks. Firstly, we extract frame-based noise from face videos in multiple colour spaces. Then local random features are extracted via random projection. Finally, Fisher vector encoding is employed to aggregate these features into global feature vectors, and a classification model is trained for detection. Experimental results on three standard face spoofing databases demonstrate the effectiveness of the approach. The equal error rate on the replay attack database is 0%. On the CASIA and MSU databases, the equal error rates are 3.52% and 0%, respectively. By combining noise-based random features and Fisher vector encoding, this method effectively resists photo, and video-based spoofing attacks.