Unsupervised Domain Adaptation for Face Anti-Spoofing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

75 Scopus Citations
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Author(s)

  • Wen Li
  • Hong Cao
  • Feiyue Huang
  • Alex C. Kot

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1794-1809
Journal / PublicationIEEE Transactions on Information Forensics and Security
Volume13
Issue number7
Online published2 Feb 2018
Publication statusPublished - Jul 2018

Abstract

Face anti-spoofing (a.k.a. presentation attack detection) has recently emerged as an active topic with great significance for both academia and industry due to the rapidly increasing demand in user authentication on mobile phones, PCs, tablets, and so on. Recently, numerous face spoofing detection schemes have been proposed based on the assumption that training and testing samples are in the same domain in terms of the feature space and marginal probability distribution. However, due to unlimited variations of the dominant conditions (illumination, facial appearance, camera quality, and so on) in face acquisition, such single domain methods lack generalization capability, which further prevents them from being applied in practical applications. In light of this, we introduce an unsupervised domain adaptation face anti-spoofing scheme to address the real-world scenario that learns the classifier for the target domain based on training samples in a different source domain. In particular, an embedding function is first imposed based on source and target domain data, which maps the data to a new space where the distribution similarity can be measured. Subsequently, the Maximum Mean Discrepancy between the latent features in source and target domains is minimized such that a more generalized classifier can be learned. State-of-the-art representations including both hand-crafted and deep neural network learned features are further adopted into the framework to quest the capability of them in domain adaptation. Moreover, we introduce a new database for face spoofing detection, which contains more than 4000 face samples with a large variety of spoofing types, capture devices, illuminations, and so on. Extensive experiments on existing benchmark databases and the new database verify that the proposed approach can gain significantly better generalization capability in cross-domain scenarios by providing consistently better anti-spoofing performance.

Research Area(s)

  • domain adaptation, Face anti-spoofing, maximum mean discrepancy

Citation Format(s)

Unsupervised Domain Adaptation for Face Anti-Spoofing. / Li, Haoliang; Li, Wen; Cao, Hong; Wang, Shiqi; Huang, Feiyue; Kot, Alex C.

In: IEEE Transactions on Information Forensics and Security, Vol. 13, No. 7, 07.2018, p. 1794-1809.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review