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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyPintoPedrSchwRoch:2012:ViFaSp
TitleVideo-Based Face Spoofing Detection through Visual Rhythm Analysis
FormatDVD, On-line.
Access Date2021, Jan. 27
Number of Files1
Size3741 KiB
Context area
Author1 Pinto, Allan da Silva
2 Pedrini, Helio
3 Schwartz, William Robson
4 Rocha, Anderson
Affiliation1 University of Campinas
2 University of Campinas
3 Universidade Federal de Minas Gerais
4 University of Campinas
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:28 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsBiometrics, Video-based Face Spoofing, Visual Rhythm, Gray-Level Co-occurrence Matrix.
AbstractRecent 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.
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