Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2957273 | IEEE ACCESS |
Keywords | DocType | Volume |
Digital forensics, facial recognition, presentation attack, noise variance | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoai Phuong Nguyen | 1 | 0 | 0.34 |
| Agnès Delahaies | 2 | 0 | 0.34 |
| Florent Retraint | 3 | 246 | 22.82 |
| Frederic Morain-Nicolier | 4 | 13 | 4.43 |