Person-Specific Face Antispoofing With Subject Domain Adaptation

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....

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Veröffentlicht in:IEEE transactions on information forensics and security Jg. 10; H. 4; S. 797 - 809
Hauptverfasser: Jianwei Yang, Zhen Lei, Dong Yi, Li, Stan Z.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2015
Schlagworte:
Adaptation models
Face
Face anti-spoofing
Face recognition
Feature extraction
person-specific
Shape
subject domain adaptation
Training
Vectors
Face anti-spoofing
subject domain adaptation
person-specific
ISSN:1556-6013, 1556-6021
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Beschreibung
Zusammenfassung: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.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2015.2403306