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Face anti-spoofing with multifeature videolet aggregation
T.A. Siddiqui, S. Bharadwaj, T.I. Dhamecha, A. Agarwal, , , N. Ratha
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 0
Pages: 1035 - 1040
Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively. © 2016 IEEE.
About the journal
JournalData powered by TypesetProceedings - International Conference on Pattern Recognition
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.