{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T03:13:57Z","timestamp":1769051637539,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T00:00:00Z","timestamp":1652572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, face recognition technology is the most widely used method for verifying an individual\u2019s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22103760","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3760","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Face Presentation Attack Detection Using Deep Background Subtraction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2023-2154","authenticated-orcid":false,"given":"Azeddine","family":"Benlamoudi","sequence":"first","affiliation":[{"name":"Laboratoire de G\u00e9nie \u00c9lectrique, Facult\u00e9 des Nouvelles Technologies de l\u2019Information et de la Communication, Universit\u00e9 Kasdi Merbah Ouargla, Ouargla 30 000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5538-7407","authenticated-orcid":false,"given":"Salah Eddine","family":"Bekhouche","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Faculty of Informatics, University of the Basque Country UPV\/EHU, 20018 San Sebastian, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-3853","authenticated-orcid":false,"given":"Maarouf","family":"Korichi","sequence":"additional","affiliation":[{"name":"Laboratoire de G\u00e9nie \u00c9lectrique, Facult\u00e9 des Nouvelles Technologies de l\u2019Information et de la Communication, Universit\u00e9 Kasdi Merbah Ouargla, Ouargla 30 000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8502-907X","authenticated-orcid":false,"given":"Khaled","family":"Bensid","sequence":"additional","affiliation":[{"name":"Laboratoire de G\u00e9nie \u00c9lectrique, Facult\u00e9 des Nouvelles Technologies de l\u2019Information et de la Communication, Universit\u00e9 Kasdi Merbah Ouargla, Ouargla 30 000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6392-7693","authenticated-orcid":false,"given":"Abdeldjalil","family":"Ouahabi","sequence":"additional","affiliation":[{"name":"UMR 1253, iBrain, INSERM, Universit\u00e9 de Tours, 37000 Tours, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9092-735X","authenticated-orcid":false,"given":"Abdenour","family":"Hadid","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie (IEMN), UMR 8520, Universit\u00e9 Polytechnique Hauts de France, Universit\u00e9 de Lille, CNRS, 59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7218-3799","authenticated-orcid":false,"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Institut d\u2019Electronique de Micro\u00e9lectronique et de Nanotechnologie (IEMN), UMR 8520, Universit\u00e9 Polytechnique Hauts de France, Universit\u00e9 de Lille, CNRS, 59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Adjabi, I., Ouahabi, A., Benzaoui, A., and Taleb-Ahmed, A. 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