Face presentation attack detection based on a statistical model of image noise - Archive ouverte HAL Access content directly
Journal Articles IEEE Access Year : 2019

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

(1) , (1) , (2) , (1)
1
2

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.

Dates and versions

hal-02394822 , version 1 (05-12-2019)

Identifiers

Cite

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

Altmetric

Share

Gmail Facebook Twitter LinkedIn More