# Biometric Recognition System (Algorithm)

## TL;DR

This paper introduces an improved fingerprint verification algorithm that enhances matching accuracy by employing image enhancement, singular point detection, minutiae extraction, and morphological noise removal, resulting in a robust biometric identification system.

## Contribution

The paper proposes a novel fingerprint verification method that achieves higher accuracy and robustness through a combination of image enhancement, rotational invariance, and feature-based matching.

## Key findings

- The proposed algorithm shows lower equal error rate (EER) compared to existing methods.
- It effectively removes noise and spurious minutiae, improving matching reliability.
- Performance metrics indicate superior accuracy and robustness in fingerprint verification.

## Abstract

Fingerprints are the most widely deployed form of biometric identification. No two individuals share the same fingerprint because they have unique biometric identifiers. This paper presents an efficient fingerprint verification algorithm which improves matching accuracy. Fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to singular point detection and minutiae extraction. Singular point is the point of maximum curvature. It is determined by the normal of each fingerprint ridge, and then following them inward towards the centre. The local ridge features known as minutiae is extracted using cross-number method to find ridge endings and ridge bifurcations. The proposed algorithm chooses a radius and draws a circle with core point as centre, making fingerprint images rotationally invariant and uniform. The radius can be varied according to the accuracy depending on the particular application. Morphological techniques such as clean, spur and H-break is employed to remove noise, followed by removing spurious minutiae. Templates are created based on feature vector extraction and databases are made for verification and identification for the fingerprint images taken from Fingerprint Verification Competition (FVC2002). Minimum Euclidean distance is calculated between saved template and the test fingerprint image template and compared with the set threshold for matching decision. For the performance evaluation of the proposed algorithm various measures, equal error rate (EER), Dmin at EER, accuracy and threshold are evaluated and plotted. The measures demonstrate that the proposed algorithm is more effective and robust.

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Source: https://tomesphere.com/paper/1812.03385