Development of an Adaptive Fingerprint Multi-Filter Enhancement Technique for Improved Reliability in Biometric Systems

Abstract

Fingerprint identification system is among the most dependable biometric identifiers due to its uniqueness and accessibility, but distorted fingerprint images usually reduce recognition accuracy. Existing multi-filter enhancement techniques improve ridge quality through a fixed sequence approach which usually leads to overenhancement and computational complexity. This study proposed an optimized multi-filter technique based on Fingerprint Noise Detection and Adaptive Removal (FINDAR) approach which detects noise patterns and applies adaptive de-noising techniques with reduced runtime and memory usage. FINDAR was implemented in MATLAB using 6000 real fingerprint images from Sokoto Coventry Fingerprint (SOCOFing) repository. Contrast enhancement and segmentation technique were employed as pre-processing steps to increase the visibility of the fingerprint images and to separate the foreground from the background, respectively. The developed model was evaluated solely using multi-noise detection accuracy and against Coherence Diffusion+Log-Gabor filter, Finger U-Net, Radial Hilbert Transform Edge Enhancement (RHLT) and Gabor+HE+CLAHE Multistage Enhancement Technique using Structural Similarity Index (SSIM), Natural Image Quality Evaluator (NIQE), Entropy, Execution time and Memory Usage, as metrics. FINDAR achieved high multi-noise detection accuracy average of 91.7% and also outperformed the benchmarked models with the highest SSIM (0.93), lowest NIQE (3.11) and highest entropy (7.02). FINDAR also demonstrated competitive runtime efficiency (17.71ms) and moderate CPU usage (295MB) compared to others. It was also observed that Finger U-Net that used deep learning approach recorded fastest runtime (13.9ms) but consumed significantly higher memory (970MB). These outcomes showed how effectively FINDAR addressed the issue of poor-quality fingerprint photos with minimal processing resources for practical biometric applications.