A Noval Approach for Face Spoof Detection using Color-Texture, Distortion and Quality Parameters

Face spoof detection technique is used in many applications to check whether the given face is spoofed or not. It helps to detect the fake faces from genuine ones. An efficient proposed method for face spoofing detection is based on color-texture, image distortion and image quality parameters. The faces are detected from a compressed format image. The color-texture information from the luminance and chrominance channels extracted using Local Binary Pattern descriptor. The image distortion and image quality parameters are extracted from the same color space. The aim of this method is to bring together the advantages of these methods inorder to improve the accuracy of face spoofing detection. Multiclass SVM classifier is used to train each features of data and detect different face spoof attack. This paper describe a novel and appealing approach for detecting the fake faces from genuine ones using a color-texture combine with image distortion and image quality parameters. More importantly, the proposed method provides more accuracy, other than the method that described in the literature. It helps to separate the original face and fake face clearly and define the type of attack