Face Anti-Spoofing System using Motion and Similarity Feature Elimination under Spoof Attacks.

From border control to mobile device unlocking applications, the practical utility of biometric system can be seriously compromised due face spoofing attacks. So, face recognition systems require greater attention to combating face spoofing attacks. As, face spoofing attacks can be easily propelled through 3D masks, video replays, and printed photos so we are presented face recognition system using motion and similarity features elimination under spoof attacks against the Replay Attack and Institute of Automation, Chinese Academy of Sciences (CASIA) databases. In this paper a calculative analysis has been done by firstly segmenting the foreground and background regions from the input video using Gaussion Mixture Model and secondly by extracting features i.e., face, eye, and nose and applied 26 image quality assessment parameters on spoof face videos under different illumination lighting conditions. The results attained using Replay Attack and CASIA databases are extremely competitive in discriminating from fake traits with paralleled viz-a-viz other approaches. Different machine learning classifiers and their comparative analysis with existing approaches has been shown.