. Given a training set consisting of videos of valid accesses, video-based spoofs, and a video for testing, first we extract a noise signature of every video (training and testing) and calculate the Fourier Spectrum on logarithmic scale for each video frame. Thereafter, we create visual rhythms for each video and train a machine learning classifier using either the pixel intensities directly as features or a summarized version of the visual rhythms using gray level co-occurrence matrices. With a trained classifier, we are able to test a visual rhythm for a given video under investigation and point out whether it is a valid access or a video-based spoof. Abstract-Recent advances on biometrics, information forensics, and security have improved the accuracy of biometric systems, mainly those based on facial information. However, an ever-growing challenge is the vulnerability of such systems to impostor attacks, in which users without access privileges try to authenticate themselves as valid users. In this work, we present a solution to video-based face spoofing to biometric systems. Such type of attack is characterized by presenting a video of a real user to the biometric system. To the best of our knowledge, this is the first attempt of dealing with video-based face spoofing based in the analysis of global information that is invariant to video content. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access. To capture the noise and obtain a compact representation, we use the Fourier spectrum followed by the computation of the visual rhythm and extraction of the graylevel co-occurrence matrices, used as feature descriptors. Results show the effectiveness of the proposed approach to distinguish between valid and fake users for video-based spoofing with nearperfect classification results.
d. S. REF proposed a data-driven method for video PAD based on Fourier analysis of the residual noise signature extracted from the input videos.
Video-Based Face Spoofing Detection through Visual Rhythm Analysis
2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Computer Science