An Effective Orchestration for Fingerprint Presentation Attack Detection
Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a n...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/16/2515 |
| _version_ | 1797432264783560704 |
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| author | Youn Kyu Lee Jongwook Jeong Dongwoo Kang |
| author_facet | Youn Kyu Lee Jongwook Jeong Dongwoo Kang |
| author_sort | Youn Kyu Lee |
| collection | DOAJ |
| description | Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques. |
| first_indexed | 2024-03-09T09:58:52Z |
| format | Article |
| id | doaj.art-eea899d09d584bd78f8ec33e6c7b3748 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T09:58:52Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-eea899d09d584bd78f8ec33e6c7b37482023-12-01T23:38:16ZengMDPI AGElectronics2079-92922022-08-011116251510.3390/electronics11162515An Effective Orchestration for Fingerprint Presentation Attack DetectionYoun Kyu Lee0Jongwook Jeong1Dongwoo Kang2Department of Computer Engineering, Hongik University, Seoul 04066, KoreaDepartment of Computer Engineering, Hongik University, Seoul 04066, KoreaDepartment of Electronic and Electrical Engineering, Hongik University, Seoul 04066, KoreaFingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.https://www.mdpi.com/2079-9292/11/16/2515fingerprint anti-spoofingpresentation attack detectionfingerprint authentication |
| spellingShingle | Youn Kyu Lee Jongwook Jeong Dongwoo Kang An Effective Orchestration for Fingerprint Presentation Attack Detection Electronics fingerprint anti-spoofing presentation attack detection fingerprint authentication |
| title | An Effective Orchestration for Fingerprint Presentation Attack Detection |
| title_full | An Effective Orchestration for Fingerprint Presentation Attack Detection |
| title_fullStr | An Effective Orchestration for Fingerprint Presentation Attack Detection |
| title_full_unstemmed | An Effective Orchestration for Fingerprint Presentation Attack Detection |
| title_short | An Effective Orchestration for Fingerprint Presentation Attack Detection |
| title_sort | effective orchestration for fingerprint presentation attack detection |
| topic | fingerprint anti-spoofing presentation attack detection fingerprint authentication |
| url | https://www.mdpi.com/2079-9292/11/16/2515 |
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