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Conference papers

Face spoofing attack detection based on the behavior of noises

Abstract : This paper aims to study the problem of spoofing attack detection for facial recognition systems. Real faces and falsified faces present in front of a security system (phone's camera in our case) have differences of micro-textures on their surface, which are exploited to discriminate face spoofing images. Our method exploits the statistic behavior of the distribution of noise's local variances, which performs differently between images of real faces and the fake ones. We test our method on two databases constructed in our laboratory. We used SVM for classification method. Experimental results show that the proposed method has an encouraging performance.
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Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Wednesday, July 1, 2020 - 3:55:24 PM
Last modification on : Thursday, October 14, 2021 - 1:10:06 PM




Hoai Phuong Nguyen, Florent Retraint, Frederic Morain-Nicolier, Agnès Delahaies. Face spoofing attack detection based on the behavior of noises. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016, Washington, United States. pp.119-123, ⟨10.1109/GlobalSIP.2016.7905815⟩. ⟨hal-02886540⟩



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