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Facial Spoofing Detection Using Temporal Texture Co-occurrence

Pan, Shi, Deravi, Farzin (2018) Facial Spoofing Detection Using Temporal Texture Co-occurrence. In: 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA). . IEEE ISBN 978-1-5386-2249-0. E-ISBN 978-1-5386-2248-3. (doi:10.1109/ISBA.2018.8311464) (KAR id:66638)

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Biometric person recognition systems based on facial images are increasingly used in a wide range of applications. However, the potential for face spoofing attacks remains a significant challenge to the security of such systems and finding better means of detecting such presentation attacks has become a necessity. In this paper, we propose a new spoofing detection method, which is based on temporal changes in texture information. A novel temporal texture descriptor is proposed to characterise the pattern of change in a short video sequence named Temporal Co-occurrence Adjacent Local Binary Pattern (TCoALBP). Experimental results using the CASIA-FA, Replay Attack and MSU-MFSD datasets; the proposed method shows the effectiveness of the proposed technique on these challenging datasets.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ISBA.2018.8311464
Uncontrolled keywords: face; three-dimensional displays; feature extraction; image color analysis; histograms; video sequences; computational complexity
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Farzin Deravi
Date Deposited: 06 Apr 2018 10:56 UTC
Last Modified: 16 Feb 2021 13:54 UTC
Resource URI: (The current URI for this page, for reference purposes)
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