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Face presentation attack detection based on a statistical model of image noise

Abstract : The vulnerability of most existing face recognition and authentication systems against face presentation attacks (a.k.a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced. The performance of the proposed approach was proven in the mentioned database. Experimental results show that, in a controlled situation, our solution performs better than the other approaches in the literature.
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https://hal-utt.archives-ouvertes.fr/hal-02394822
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Thursday, December 5, 2019 - 8:48:01 AM
Last modification on : Tuesday, July 21, 2020 - 9:26:05 AM

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Hoai Phuong Nguyen, Anges Delahaies, Florent Retraint, Frederic Morain-Nicolier. Face presentation attack detection based on a statistical model of image noise. IEEE Access, IEEE, 2019, 7, pp.175429-175442. ⟨10.1109/ACCESS.2019.2957273⟩. ⟨hal-02394822⟩

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