Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security
Biometric systems such as fingerprint recognition encounter significant challenges under wet conditions or small fingerprints, where noise degrades recognition accuracy. These challenges increase false acceptance rates (FARs) and false rejection rates (FRRs) as conventional denoising models designed...
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MDPI AG
2025-05-01
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| author | Mao-Hsiu Hsu Ying-Hong Shi |
| author_facet | Mao-Hsiu Hsu Ying-Hong Shi |
| author_sort | Mao-Hsiu Hsu |
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| description | Biometric systems such as fingerprint recognition encounter significant challenges under wet conditions or small fingerprints, where noise degrades recognition accuracy. These challenges increase false acceptance rates (FARs) and false rejection rates (FRRs) as conventional denoising models designed for larger fingerprints cannot handle the smaller and noisier samples in portable and embedded devices. In this study, we collected 71,188 wet–dry fingerprints using a capacitive sensor. Fingerprints in sizes of 176 × 36, 88 × 88, and 80 × 100 pixels were preprocessed by padding and cropping them to a uniform size of 48 × 48 pixels. Preprocessing was conducted to standardize and augment the data and enhance the model’s ability to generalize across diverse data types. We developed a wet fingerprint denoising network (WFDN), a multi-stage neural network designed to improve wet fingerprint quality for small and large samples. By integrating scale-invariant feature transform, WFDN effectively restores critical minutiae and significantly enhances feature preservation compared with existing models. The network also incorporates an automatic label classifier and cyclic multi-variate functions to reduce noise. Despite its compact architecture, WFDN demonstrates superior performance, reducing the FRR from 19.6 to 8.4% for small fingerprints. Moreover, assessment results using NIST fingerprint image quality 2.0 (NFIQ2) for larger fingerprints show notable improvements in system reliability. The proposed model improves biometric processing significantly. WFDN represents a significant advancement in fingerprint-based identification, offering improved performance and robustness in challenging conditions. |
| format | Article |
| id | doaj-art-53d33c9f2ec1411fa99fc8689175624b |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-53d33c9f2ec1411fa99fc8689175624b2025-08-20T03:24:40ZengMDPI AGEngineering Proceedings2673-45912025-05-019216410.3390/engproc2025092064Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric SecurityMao-Hsiu Hsu0Ying-Hong Shi1Department of Electro-Optical Engineering, National Formosa University, Yunlin 632, TaiwanDepartment of Electro-Optical Engineering, National Formosa University, Yunlin 632, TaiwanBiometric systems such as fingerprint recognition encounter significant challenges under wet conditions or small fingerprints, where noise degrades recognition accuracy. These challenges increase false acceptance rates (FARs) and false rejection rates (FRRs) as conventional denoising models designed for larger fingerprints cannot handle the smaller and noisier samples in portable and embedded devices. In this study, we collected 71,188 wet–dry fingerprints using a capacitive sensor. Fingerprints in sizes of 176 × 36, 88 × 88, and 80 × 100 pixels were preprocessed by padding and cropping them to a uniform size of 48 × 48 pixels. Preprocessing was conducted to standardize and augment the data and enhance the model’s ability to generalize across diverse data types. We developed a wet fingerprint denoising network (WFDN), a multi-stage neural network designed to improve wet fingerprint quality for small and large samples. By integrating scale-invariant feature transform, WFDN effectively restores critical minutiae and significantly enhances feature preservation compared with existing models. The network also incorporates an automatic label classifier and cyclic multi-variate functions to reduce noise. Despite its compact architecture, WFDN demonstrates superior performance, reducing the FRR from 19.6 to 8.4% for small fingerprints. Moreover, assessment results using NIST fingerprint image quality 2.0 (NFIQ2) for larger fingerprints show notable improvements in system reliability. The proposed model improves biometric processing significantly. WFDN represents a significant advancement in fingerprint-based identification, offering improved performance and robustness in challenging conditions.https://www.mdpi.com/2673-4591/92/1/64biometric securitywet fingerprintsdenoisingneural networksfingerprint recognition |
| spellingShingle | Mao-Hsiu Hsu Ying-Hong Shi Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security Engineering Proceedings biometric security wet fingerprints denoising neural networks fingerprint recognition |
| title | Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security |
| title_full | Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security |
| title_fullStr | Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security |
| title_full_unstemmed | Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security |
| title_short | Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security |
| title_sort | optimizing wet fingerprint denoising net for enhanced biometric security |
| topic | biometric security wet fingerprints denoising neural networks fingerprint recognition |
| url | https://www.mdpi.com/2673-4591/92/1/64 |
| work_keys_str_mv | AT maohsiuhsu optimizingwetfingerprintdenoisingnetforenhancedbiometricsecurity AT yinghongshi optimizingwetfingerprintdenoisingnetforenhancedbiometricsecurity |