Face Presentation Attack Detection Using Deep Background Subtraction

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....

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Autores: Benlamoudi, Azeddine, Bekhouche, Salah Eddine, Korichi, Maarouf, Bensid, Khaled, Ouahabi, Abdeldjalil, Hadid, Abdenour, Taleb-Ahmed, Abdelmalik
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/56813
Acceso en línea:http://hdl.handle.net/10810/56813
Access Level:acceso abierto
Palabra clave:biometrics
face presentation attack
deep learning
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spelling Face Presentation Attack Detection Using Deep Background SubtractionBenlamoudi, AzeddineBekhouche, Salah EddineKorichi, MaaroufBensid, KhaledOuahabi, AbdeldjalilHadid, AbdenourTaleb-Ahmed, Abdelmalikbiometricsface presentation attackdeep learningCurrently, 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.MDPI2022202220222022info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/56813reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/1424-8220/22/10/3760/htminfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).oai:addi.ehu.eus:10810/568132026-06-18T09:23:17Z
dc.title.none.fl_str_mv Face Presentation Attack Detection Using Deep Background Subtraction
title Face Presentation Attack Detection Using Deep Background Subtraction
spellingShingle Face Presentation Attack Detection Using Deep Background Subtraction
Benlamoudi, Azeddine
biometrics
face presentation attack
deep learning
title_short Face Presentation Attack Detection Using Deep Background Subtraction
title_full Face Presentation Attack Detection Using Deep Background Subtraction
title_fullStr Face Presentation Attack Detection Using Deep Background Subtraction
title_full_unstemmed Face Presentation Attack Detection Using Deep Background Subtraction
title_sort Face Presentation Attack Detection Using Deep Background Subtraction
dc.creator.none.fl_str_mv Benlamoudi, Azeddine
Bekhouche, Salah Eddine
Korichi, Maarouf
Bensid, Khaled
Ouahabi, Abdeldjalil
Hadid, Abdenour
Taleb-Ahmed, Abdelmalik
author Benlamoudi, Azeddine
author_facet Benlamoudi, Azeddine
Bekhouche, Salah Eddine
Korichi, Maarouf
Bensid, Khaled
Ouahabi, Abdeldjalil
Hadid, Abdenour
Taleb-Ahmed, Abdelmalik
author_role author
author2 Bekhouche, Salah Eddine
Korichi, Maarouf
Bensid, Khaled
Ouahabi, Abdeldjalil
Hadid, Abdenour
Taleb-Ahmed, Abdelmalik
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv biometrics
face presentation attack
deep learning
topic biometrics
face presentation attack
deep learning
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/56813
url http://hdl.handle.net/10810/56813
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/1424-8220/22/10/3760/htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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