FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning
Face recognition technology has been widely used due to the convenience it provides. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/10/2199 |
| _version_ | 1797600314454441984 |
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| author | Enoch Solomon Krzysztof J. Cios |
| author_facet | Enoch Solomon Krzysztof J. Cios |
| author_sort | Enoch Solomon |
| collection | DOAJ |
| description | Face recognition technology has been widely used due to the convenience it provides. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers. One, random forest, uses the identified by us seven no-reference image quality features derived from face images and its results are fused with a deep learning classifier results that uses entire face images as input. Extensive experiments were performed to compare FASS with state-of-the-art anti-spoofing systems on five benchmark datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. The results show that FASS outperforms all face anti-spoofing systems based on image quality features and is also more accurate than many of the state-of-the-art systems based on deep learning. |
| first_indexed | 2024-03-11T03:47:45Z |
| format | Article |
| id | doaj.art-7066b8f3a7a24a24b018c9881ff32c2b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:47:45Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7066b8f3a7a24a24b018c9881ff32c2b2023-11-18T01:09:07ZengMDPI AGElectronics2079-92922023-05-011210219910.3390/electronics12102199FASS: Face Anti-Spoofing System Using Image Quality Features and Deep LearningEnoch Solomon0Krzysztof J. Cios1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 , USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 , USAFace recognition technology has been widely used due to the convenience it provides. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers. One, random forest, uses the identified by us seven no-reference image quality features derived from face images and its results are fused with a deep learning classifier results that uses entire face images as input. Extensive experiments were performed to compare FASS with state-of-the-art anti-spoofing systems on five benchmark datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. The results show that FASS outperforms all face anti-spoofing systems based on image quality features and is also more accurate than many of the state-of-the-art systems based on deep learning.https://www.mdpi.com/2079-9292/12/10/2199anti-spoofing systembiometrics securitydeep learningface image quality featuresensemble learningrandom forest |
| spellingShingle | Enoch Solomon Krzysztof J. Cios FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning Electronics anti-spoofing system biometrics security deep learning face image quality features ensemble learning random forest |
| title | FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning |
| title_full | FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning |
| title_fullStr | FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning |
| title_full_unstemmed | FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning |
| title_short | FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning |
| title_sort | fass face anti spoofing system using image quality features and deep learning |
| topic | anti-spoofing system biometrics security deep learning face image quality features ensemble learning random forest |
| url | https://www.mdpi.com/2079-9292/12/10/2199 |
| work_keys_str_mv | AT enochsolomon fassfaceantispoofingsystemusingimagequalityfeaturesanddeeplearning AT krzysztofjcios fassfaceantispoofingsystemusingimagequalityfeaturesanddeeplearning |