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 they contain less noise%3B (2) they have fewer discontinuities in ridge patterns%3B (3) the ridge-valley pattern is less distinct%3B and (4) they pose an interoperability problem%2C 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%2C 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%2C 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%25 on the PolyU contactless fingerprint dataset%2C demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience%2C proving to be an effective and feasible solution for contactless fingerprint-based identification systems.?quality=80&w=800)
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