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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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3760 |
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| author | Azeddine Benlamoudi Salah Eddine Bekhouche Maarouf Korichi Khaled Bensid Abdeldjalil Ouahabi Abdenour Hadid Abdelmalik Taleb-Ahmed |
| author_facet | Azeddine Benlamoudi Salah Eddine Bekhouche Maarouf Korichi Khaled Bensid Abdeldjalil Ouahabi Abdenour Hadid Abdelmalik Taleb-Ahmed |
| author_sort | Azeddine Benlamoudi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T01:54:21Z |
| format | Article |
| id | doaj.art-c2509710e5a540368195c7079969ee9c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:54:21Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2509710e5a540368195c7079969ee9c2023-11-23T13:00:43ZengMDPI AGSensors1424-82202022-05-012210376010.3390/s22103760Face Presentation Attack Detection Using Deep Background SubtractionAzeddine Benlamoudi0Salah Eddine Bekhouche1Maarouf Korichi2Khaled Bensid3Abdeldjalil Ouahabi4Abdenour Hadid5Abdelmalik Taleb-Ahmed6Laboratoire de Génie Électrique, Faculté des Nouvelles Technologies de l’Information et de la Communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, AlgeriaDepartment of Computer Science and Artificial Intelligence, Faculty of Informatics, University of the Basque Country UPV/EHU, 20018 San Sebastian, SpainLaboratoire de Génie Électrique, Faculté des Nouvelles Technologies de l’Information et de la Communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, AlgeriaLaboratoire de Génie Électrique, Faculté des Nouvelles Technologies de l’Information et de la Communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, AlgeriaUMR 1253, iBrain, INSERM, Université de Tours, 37000 Tours, FranceInstitut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, Université de Lille, CNRS, 59313 Valenciennes, FranceInstitut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université Polytechnique Hauts de France, Université de Lille, CNRS, 59313 Valenciennes, FranceCurrently, 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.https://www.mdpi.com/1424-8220/22/10/3760biometricsface presentation attackdeep learning |
| spellingShingle | Azeddine Benlamoudi Salah Eddine Bekhouche Maarouf Korichi Khaled Bensid Abdeldjalil Ouahabi Abdenour Hadid Abdelmalik Taleb-Ahmed Face Presentation Attack Detection Using Deep Background Subtraction Sensors biometrics face presentation attack deep learning |
| title | 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_short | Face Presentation Attack Detection Using Deep Background Subtraction |
| title_sort | face presentation attack detection using deep background subtraction |
| topic | biometrics face presentation attack deep learning |
| url | https://www.mdpi.com/1424-8220/22/10/3760 |
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