期刊
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
卷 10, 期 4, 页码 797-809出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2015.2403306
关键词
Face anti-spoofing; person-specific; subject domain adaptation
资金
- National Natural Science Foundation of China [61203267, 61375037, 61473291]
- National Science and Technology Support Program [2013BAK02B01]
- Chinese Academy of Sciences, Beijing, China [KGZD-EW-102-2]
- AuthenMetric Research and Development Funds
Face antispoofing is important to practical face recognition systems. In previous works, a generic antispoofing classifier is trained to detect spoofing attacks on all subjects. However, due to the individual differences among subjects, the generic classifier cannot generalize well to all subjects. In this paper, we propose a person-specific face antispoofing approach. It recognizes spoofing attacks using a classifier specifically trained for each subject, which dismisses the interferences among subjects. Moreover, considering the scarce or void fake samples for training, we propose a subject domain adaptation method to synthesize virtual features, which makes it tractable to train well-performed individual face antispoofing classifiers. The extensive experiments on two challenging data sets: 1) CASIA and 2) REPLAY-ATTACK demonstrate the prospect of the proposed approach.
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