A Robust Algorithm for Contactless Fingerprint Enhancement and Matching
This addresses the challenge of accurate fingerprint identification in contactless systems, which is incremental as it builds on existing techniques for a specific domain.
The paper tackled the problem of enhancing and matching contactless fingerprint images, which have distinct characteristics like less distinct ridge patterns, by proposing a novel solution that improved minutiae detection and matching, achieving a minimum Equal Error Rate of 2.84% on the PolyU dataset.
Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise; (2) they have fewer discontinuities in ridge patterns; (3) the ridge-valley pattern is less distinct; and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device. These properties present significant challenges for the enhancement of contactless fingerprint images. In this study, we propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection through improved frequency estimation and a new region-quality-based minutia extraction algorithm. In addition, we introduce an efficient and highly accurate minutiae-based encoding and matching algorithm. We validate the effectiveness of our approach through extensive experimental testing. Our method achieves a minimum Equal Error Rate (EER) of 2.84\% on the PolyU contactless fingerprint dataset, demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience, proving to be an effective and feasible solution for contactless fingerprint-based identification systems.