Robust rotation-invariant fingerprint image hashing using fast wavelet transform and fractal coding for noise filtering with minimal run-time

Biometric data, particularly fingerprints, provide a reliable means of personal identification; however, protecting sensitive biometric templates against unauthorized access, tampering, and reconstruction remains a significant challenge in biometric security systems. Existing fingerprint hashing approaches often face limitations in terms of computational efficiency, robustness to geometric distortions, and preservation of discriminative ridge features. To address these limitations, this article proposes a fingerprint hashing technique that integrates the Fast Wavelet Transform (FWT), Fourier-Mellin Transform (FMT), and fractal coding to improve robustness and computational efficiency. In the proposed framework, FWT is employed for noise reduction and multi-resolution feature extraction, effectively preserving essential ridge structures and improving image quality. The FMT is utilized to achieve rotation-invariant feature representation, while fractal coding performs efficient image compression and enhances robustness against distortions and dimensional variations. The integration of FWT with fractal coding further reduces computational complexity while maintaining strong discriminative capability in the generated hash codes. Comprehensive experiments conducted on the FVC2002, FVC2004, and SOCOFing fingerprint databases demonstrate the effectiveness of the proposed hashing framework. The method achieves strong discriminability with an Equal Error Rate (EER) as low as 0.3864%, while maintaining low False Match Rate (FMR) and False Non-Match Rate (FNMR). In addition, the proposed approach significantly reduces computational cost, achieving an average execution time of 2.507 seconds compared to 1048.888 seconds in existing fractal-coding-based hashing approaches. Performance evaluation using PSNR, SNR, SSIM, entropy, mutual information, and edge preservation metrics confirms that the proposed framework improves noise resilience, preserves structural fingerprint features, and maintains robustness against geometric variations. These results demonstrate that the proposed approach generates compact, secure, and robust fingerprint hashes suitable for biometric authentication, forensic identification, and digital identity verification systems.