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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2012/07.13.21.16
%2 sid.inpe.br/sibgrapi/2012/07.13.21.16.53
%T Video-Based Face Spoofing Detection through Visual Rhythm Analysis
%D 2012
%A Pinto, Allan da Silva,
%A Pedrini, Helio,
%A Schwartz, William Robson,
%A Rocha, Anderson,
%@affiliation University of Campinas
%@affiliation University of Campinas
%@affiliation Universidade Federal de Minas Gerais
%@affiliation University of Campinas
%E Freitas, Carla Maria Dal Sasso,
%E Sarkar, Sudeep,
%E Scopigno, Roberto,
%E Silva, Luciano,
%B Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)
%C Ouro Preto
%8 Aug. 22-25, 2012
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Biometrics, Video-based Face Spoofing, Visual Rhythm, Gray-Level Co-occurrence Matrix.
%X 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 gray-level 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 near-perfect classification results.
%@language en
%3 paper_camera_ready.pdf


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