No Feature Left Behind: Filling the Gap in Fingerprint Frequency Estimation

Fingerprint recognition remains a cornerstone of biometric identification systems. However, the critical task of ridge-line frequency estimation has been surprisingly underexplored. To address this research gap, I introduce the first publicly available benchmark for fingerprint frequency estimation, a pioneering resource that includes both high- and low-quality fingerprints with meticulously labeled ground truth features: segmentation masks, orientation fields, and frequency maps. Furthermore, two novel frequency estimation methods are proposed: one that significantly enhances a well-known frequency estimation method based on traditional image processing techniques and another that, for the first time, applies deep learning to this context. Experimental results on the new benchmark demonstrate that both methods surpass existing state-of-the-art techniques, particularly in challenging low-quality fingerprint scenarios. By providing an open-source implementation and a comprehensive benchmark, this work sets a new standard for the evaluation of frequency estimation methods, fostering further research and development in this crucial area of fingerprint recognition.