Classification Of Captured And Recaptured Images To Detect Photograph Spoofing

In this paper, a new face anti-spoofing approach, which is based on analysis of contrast and texture characteristics of captured and recaptured images, is proposed to detect photograph spoofing. Since photo image is a recaptured image, it may show quite different contrast and texture characteristics when compared to a real face image. In a spoofing attempt, image rotation is quite possible. Therefore, in this paper, a rotation invariant local binary pattern variance (LBPV) based method is selected to be used. The approach is tested on the publicly available NUAA photo-impostor database, which is constructed under illumination and place change. The results show that the approach is competitive with other existing methods tested on the same database. It is especially useful for conditions when photos are held by hand to spoof the system. Since an LBPV based method is used, it is robust to illumination changes. It is non-intrusive and simple.