Unleashing the Power of Simplicity: A Minimalist Strategy for State-of-the-Art Fingerprint Enhancement

TL;DR

This paper introduces a minimalist fingerprint enhancement approach with two novel methods that outperform complex techniques, emphasizing simplicity for better accuracy and noise reduction in challenging fingerprint images.

Contribution

The paper presents two new simple, effective fingerprint enhancement methods that outperform existing complex state-of-the-art techniques.

Findings

Outperforms complex methods on latent fingerprint data

Produces clearer and less noisy fingerprint images

Open-source implementation promotes reproducibility

Abstract

Fingerprint recognition systems, which rely on the unique characteristics of human fingerprints, are essential in modern security and verification applications. Accurate minutiae extraction, a critical step in these systems, depends on the quality of fingerprint images. Despite recent improvements in fingerprint enhancement techniques, state-of-the-art methods often struggle with low-quality fingerprints and can be computationally demanding. This paper presents a minimalist approach to fingerprint enhancement, prioritizing simplicity and effectiveness. Two novel methods are introduced: a contextual filtering method and a learning-based method. These techniques consistently outperform complex state-of-the-art methods, producing clearer, more accurate, and less noisy images. The effectiveness of these methods is validated using a challenging latent fingerprint database. The open-source implementation of these techniques not only fosters reproducibility but also encourages further advancements in the field. The findings underscore the importance of simplicity in achieving high-quality fingerprint enhancement and suggest that future research should balance complexity and practical benefits.