Version 1
: Received: 4 April 2022 / Approved: 6 April 2022 / Online: 6 April 2022 (07:55:23 CEST)
How to cite:
Benlamoudi, A.; Bekhouche, S. E.; Korichi, M.; Bensid, K.; Ouahabi, A.; Hadid, A.; Taleb-Ahmed, A. Face Spoof Attack Detection using Deep Background Subtraction. Preprints2022, 2022040033. https://doi.org/10.20944/preprints202204.0033.v1
Benlamoudi, A.; Bekhouche, S. E.; Korichi, M.; Bensid, K.; Ouahabi, A.; Hadid, A.; Taleb-Ahmed, A. Face Spoof Attack Detection using Deep Background Subtraction. Preprints 2022, 2022040033. https://doi.org/10.20944/preprints202204.0033.v1
Benlamoudi, A.; Bekhouche, S. E.; Korichi, M.; Bensid, K.; Ouahabi, A.; Hadid, A.; Taleb-Ahmed, A. Face Spoof Attack Detection using Deep Background Subtraction. Preprints2022, 2022040033. https://doi.org/10.20944/preprints202204.0033.v1
APA Style
Benlamoudi, A., Bekhouche, S. E., Korichi, M., Bensid, K., Ouahabi, A., Hadid, A., & Taleb-Ahmed, A. (2022). Face Spoof Attack Detection using Deep Background Subtraction. Preprints. https://doi.org/10.20944/preprints202204.0033.v1
Chicago/Turabian Style
Benlamoudi, A., Abdenour Hadid and Abdelmalik Taleb-Ahmed. 2022 "Face Spoof Attack Detection using Deep Background Subtraction" Preprints. https://doi.org/10.20944/preprints202204.0033.v1
Abstract
Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.
Keywords
Biometrics; Face spoofing; CNN; BS; ResNet-50
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.