Face-Fake-Net: The Deep Learning Method for Image Face Anti-Spoofing Detection : 45
Author | Alshaikhli M. |
Author | Elharrouss O. |
Author | Al-Maadeed, Somaya |
Author | Bouridane A. |
Available date | 2022-05-19T10:23:07Z |
Publication Date | 2021 |
Publication Name | Proceedings - European Workshop on Visual Information Processing, EUVIP |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/EUVIP50544.2021.9484023 |
Abstract | Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing. |
Sponsor | This work was supported by the NPRP from the Qatar National Research Fund (a member of the Qatar Foundation) under the National Priorities Research Program (NPRP) under Grant PRP12S-0312-190332. The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Learning systems Channel-level Generalization capability Growing demand Learning methods Real-world scenario Recognition systems Target domain User identification Deep learning |
Type | Conference Paper |
Volume Number | 2021-June |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Computer Science & Engineering [2281 items ]