# Enhancing Fingerprint Recognition Systems: Comparative Analysis of Biometric Authentication Algorithms and Techniques for Improved Accuracy and Reliability

arXiv: https://arxiv.org/abs/2412.14404 · HTML: https://mlsanity.com/paper/2412.14404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Sokoto Coventry Fingerprint Dataset](https://mlsanity.com/paper/1807.10609/) (2018)
- [FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network](https://mlsanity.com/paper/1907.12956/) (2019)

## Abstract (arXiv)

Fingerprint recognition systems stand as pillars in the realm of biometric authentication, providing indispensable security measures across various domains. This study investigates integrating Convolutional Neural Networks (CNNs) with Gabor filters to improve fingerprint recognition accuracy and robustness. Leveraging a diverse dataset sourced from the Sokoto Coventry Fingerprint Dataset, our experiments meticulously evaluate the efficacy of different classification algorithms. Our findings underscore the supremacy of CNN-based approaches, boasting an impressive overall accuracy of 94%. Furthermore, the amalgamation of Gabor filters with CNN architectures unveils promising strides in discerning altered fingerprints, illuminating new pathways for enhancing biometric authentication systems. While the CNN-Gabor fusion showcases commendable performance, our exploration of hybrid approaches combining multiple classifiers reveals nuanced outcomes. Despite these mixed results, our study illuminates the transformative potential of deep learning methodologies in reshaping the landscape of fingerprint recognition. Through rigorous experimentation and insightful analysis, this research not only contributes to advancing biometric authentication technologies but also sheds light on the intricate interplay between traditional feature extraction methods and cutting-edge deep learning architectures. These findings offer actionable insights for optimizing fingerprint recognition systems for real-world deployment, paving the way for enhanced security and reliability in diverse applications.