Face Presentation Attack Detection Using Deep Background Subtraction

Sensors

Salah Eddine Bekhouche¹  Abdenour Hadid²  Abdelmalik Taleb-Ahmed²  Maarouf Korichi³  Azeddine Benlamoudi³  Khaled Bensid³  Abdeldjalil Ouahabi⁴ 

[1] Department of Computer Science and Artificial Intelligence, Faculty of Informatics, University of the Basque Country UPV/EHU, 20018 San Sebastian, Spain;Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, Université de Lille, CNRS, 59313 Valenciennes, France;Laboratoire de Génie Électrique, Faculté des Nouvelles Technologies de l’Information et de la Communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, Algeria;UMR 1253, iBrain, INSERM, Université de Tours, 37000 Tours, France;

关键词: biometrics;    face presentation attack;    deep learning;   

DOI  :  10.3390/s22103760

来源: DOAJ

【 摘 要 】

Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (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 are very interesting and are much better than those obtained by state-of-the-art methods. For instance, 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%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases.

【 授权许可】

Unknown