Biblio

Imposters gain unauthorized access to biometric recognition systems using fake biometric data of the legitimate user termed as spoofing. Spoofing of face recognition systems is done by photographs, 3D models and videos of the user. Attack video contains noise from the acquisition process. In this work, we use noise residual content of the video in order to detect spoofed videos. We take advantage of wavelet transform for representing the noise video. Samples of the noise video, termed as visual rhythm image is created for each video. Local Binary Pattern (LBP) and uniform Local Binary Pattern (LBPu2) are extracted from the visual rhythm image followed by classification using Support Vector Machine (SVM). Large size of video from which a number of frames are used for analysis results in huge execution timing. In this work the spoof detection algorithm is applied on various levels of subsections of the video frames resulting in reduced execution timing with reasonable detection accuracies.