Optimizing Wet Fingerprint Denoising Net for Enhanced Biometric Security - Mao-Hsiu Hsu; Ying-Hong Shi (Engineering Proceedings)
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.