Fusion of Methods Based on Minutiae, Ridges and Pores for Robust Fingerprint Recognition

TL;DR

This paper proposes a fusion approach combining minutiae, ridges, and pore features for fingerprint recognition, significantly improving accuracy and robustness over individual methods using advanced sensor data.

Contribution

It introduces a novel fusion technique integrating third-level pore features with traditional minutiae and ridge-based methods for enhanced fingerprint recognition.

Findings

Approximately 16% reduction in Equal Error Rate with the fusion method

Effective integration of third-level pore features improves recognition robustness

Validated on the PolyU HRF database with promising results

Abstract

The use of physical and behavioral characteristics for human identification is known as biometrics. Among the many biometrics traits available, the fingerprint is the most widely used. The fingerprint identification is based on the impression patterns, as the pattern of ridges and minutiae, characteristics of first and second levels respectively. The current identification systems use these two levels of fingerprint features due to the low cost of the sensors. However, due the recent advances in sensor technology, it is possible to use third level features present within the ridges, such as the perspiration pores. Recent studies have shown that the use of third-level features can increase security and fraud protection in biometric systems, since they are difficult to reproduce. In addition, recent researches have also focused on multibiometrics recognition due to its many advantages. The goal of this work was to apply fusion techniques for fingerprint recognition in order to combine minutiae, ridges and pore-based methods and, thus, provide more robust biometrics recognition systems. We evaluated isotropic-based and adaptive-based automatic pore extraction methods and the fusion of pore-based method with the identification methods based on minutiae and ridges. The experiments were performed on the public database PolyU HRF and showed a reduction of approximately 16% in the Equal Error Rate compared to the best results obtained by the methods individually.