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Amilab IJCB 2011 Poster

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Amilab IJCB 2011 Poster

  1. 1. Fusion of multiple clues for photo-attack detection in face recognition systemsRoberto Tronci , Daniele Muntoni , Gianluca Fadda , Maurizio Pili , Nicola Sirena , Marco Ristori , 1,2 1,2 1 1 1 1 Gabriele Murgia , Fabio Roli 1 2 1 Ambient Intelligence Lab, Sardinia DistrICT, Sardegna Ricerche, ITALY 2 DIEE, Dept. Electric and Electronic Engineering, University of Cagliari, ITALY {roberto.tronci, muntoni, fadda, maurizio.pili, sirena, gabriele.murgia, ristori} {roberto.tronci, daniele.muntoni, roli} Introduction SARDEGNA RICERCHE We faced the problem of detecting 2-D face spoofing attacks performed by placing a printed photo of a real user in front of the camera. For this type of attack it is not possible to relay just on the face movements as a clue of vitality because the attacker can easily simulate IJCB2011 such a case, and also because real users often show a “low vitality” during the authentication session. In this paper, we perform both video and static analysis in order to employ complementary information about motion, texture and liveness and consequently to obtain a more robust classification.Our approach ClassificationAmILabs Spoof Detector implements a multi-clue approach. At classification stage scores are computed over a sliding window of a few seconds of video.Static analysis tackles the visual characteristics of a photo attack. Within this window, static analysis results in FxN scores (F frames and NThe visual representations that we propose to use are: Color and Edge visual representations). A unique score is computed through a DSCDirectivity Descriptor, Fuzzy Color and Texture Histogram, MPEG-7 algorithm. :Descriptors (like Scalable Color and Edge Histogram), Gabor Texture, S sa = 1− ⋅min { S i , f }⋅max { S i , f } i∈[1, N ] , f ∈[1, F ]Tamura Texture, RGB and HSV Histograms, and JPEG Histogram.For each frame, each of the above mentioned visual representations result Finally, fusion between static and video analysis is performed as:in a specific score.Video analysis aims to detect vitality clues. Clues examined in this workare motion analysis of the scene and the number of eye blinks that are S = { ⋅S sa1 −  ⋅S bl ,  1⋅S sa  2⋅S bl  3⋅S m , if S m is high if S m is lowrepresented by two independent scores. S sa Still Frame Characteristic analysis D S S S bl C Blink detection Sm LOW? Global motion YesExperimental results: the face spoof competitionFor our experiments we used the Print-Attack Replay Database developedfor the IJCB 2011 Competition on counter measures to 2D facial spoofingattacks from the Idiap Research Institute.Although static analysis alone easily achieves a perfect separation inthe test set, we enhanced its classification with the video analysis inorder to grant performances even with higher quality printed photos orhigh quality displays (smart-phones, tablets and other modern portable¿devices). ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ ¿ −¿= f k  x i  omega −¿ , i s¿ ik −¿=¿ ¿= f k  x i  omega¿ , S ¿ k i s¿ ik ¿=¿ S ¿k Introduction of video analysis results in lower performances in terms of separation of scores distributions. However, the proposed fusion scheme still proved to be very effective and robust. The contribution of video analysis in terms of robust classification will be further investigated in future works.Contacts Ambient Intelligence Lab - Edificio 1, Loc. Piscinamanna, 09010 Pula (CA), Italy - Tel. +39 070 9243 2682