Face spoofing detection using local binary patterns and Fisher Score

Todays biometric systems are vulnerable to spoof attacks made by non-real faces. The problem is when a person shows in front of camera a print photo or a picture from cell phone. We study in this paper an anti-spoofing solution for distinguishing between ´live´ and ´fake´ faces. In our approach we used overlapping block LBP operator to extract features in each region of the image. To reduce the features we used Fisher-Score. Finally, we used a nonlinear Support Vector Machine (SVM) classifier with kernel function for determining whether the input image corresponds to a live face or not. Our experimental analysis on a publicly available NUAA and CASIA face anti-spoofing databases following the standard protocols showed good results.