A Robust Algorithm for Contactless Fingerprint Enhancement and Matching

TL;DR

This paper presents a new contactless fingerprint enhancement and matching algorithm that improves minutiae detection accuracy and achieves state-of-the-art performance with a low EER on a standard dataset.

Contribution

It introduces novel enhancement, minutiae extraction, and encoding algorithms tailored for contactless fingerprint images, addressing their unique challenges.

Findings

Achieves a minimum EER of 2.84% on PolyU dataset

Outperforms existing state-of-the-art contactless fingerprint methods

Demonstrates high precision and robustness in contactless fingerprint identification

Abstract

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.