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

2026-03-19

AI Summary

This work addresses the limitations of existing fingerprint enhancement methods, which often incur high computational costs and deliver suboptimal results on low-quality fingerprints, thereby hindering accurate minutiae extraction. To overcome these challenges, the authors propose a minimalist enhancement strategy that integrates contextual filtering with a lightweight learning mechanism, achieving efficient and high-quality enhancement without relying on complex models. Evaluated on challenging latent fingerprint datasets, the proposed method significantly improves image clarity, effectively suppresses noise, and achieves a superior balance between practicality and performance compared to current state-of-the-art approaches. The implementation has been made publicly available to facilitate reproducibility and real-world application.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Privacy
• Search and Optimization: Local Search

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• fingerprint enhancement
• low-quality fingerprints
• minutiae extraction
• computational complexity
• image quality

Innovation

• minimalist approach
• fingerprint enhancement
• contextual filtering
• learning-based method
• latent fingerprint