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Face anti-spoofing via motion magnification and multifeature videolet aggregation

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dc.contributor.author Bharadwaj, Samarth
dc.contributor.author Dhamecha, Tejas I
dc.contributor.author Vatsa, Mayank
dc.contributor.author Singh, Richa
dc.date.accessioned 2014-06-03T05:50:38Z
dc.date.available 2014-06-03T05:50:38Z
dc.date.issued 2014-06-03T05:50:38Z
dc.identifier.uri https://repository.iiitd.edu.in/jspui/handle/123456789/138
dc.description.abstract For robust face biometrics, a reliable anti-spoofing approach has become an essential pre-requisite against attacks. While spoofing attacks are possible with any biometric modality, face spoofing attacks are relatively easy which makes facial biometrics especially vulnerable. This paper presents a new framework for face spoofing detection in videos using motion magnification and multifeature evidence aggregation in a windowed fashion. Micro- and macro- facial expressions commonly exhibited by subjects are first magnified using Eulerian motion magnification. Next, two feature extraction algorithms, a configuration of local binary pattern and motion estimation using histogram of oriented optical flow, are used to encode texture and motion (liveness) properties respectively. Multifeature windowed videolet aggregation of these two orthogonal features, coupled with support vector machine classification provides robustness to different attacks. The proposed approach is evaluated and compared with existing algorithms on publicly available Print Attack, Replay Attack, and CASIA-FASD databases. The proposed algorithm yields state-of-the-art performance and robust generalizability with low computational complexity. en_US
dc.language.iso en_US en_US
dc.relation.ispartofseries IIITD-TR-2014-002
dc.subject Face recognition en_US
dc.subject Anti-spoofing en_US
dc.subject Obfuscation en_US
dc.subject Motion magnification en_US
dc.title Face anti-spoofing via motion magnification and multifeature videolet aggregation en_US
dc.type Technical Report en_US

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