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.