Title :
Person-Specific Face Antispoofing With Subject Domain Adaptation
Author :
Jianwei Yang ; Zhen Lei ; Dong Yi ; Li, Stan Z.
Author_Institution :
Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
Abstract :
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.
Keywords :
face recognition; image classification; CASIA; REPLAY-ATTACK demonstrate; face antispoofing classifier; face recognition system; fake sample; generic antispoofing classifier; generic classifier; person-specific face antispoofing approach; spoofing attack; subject domain adaptation method; virtual feature; Adaptation models; Face; Face recognition; Feature extraction; Shape; Training; Vectors; Face anti-spoofing; person-specific; subject domain adaptation;
Journal_Title :
Information Forensics and Security, IEEE Transactions on
DOI :
10.1109/TIFS.2015.2403306