An Effective Fingerprint Verification Technique

TL;DR

This paper introduces a novel fingerprint verification method combining minutiae clustering, graph theory, and neural networks to improve accuracy and invariance in fingerprint recognition.

Contribution

It presents a new fingerprint verification approach integrating graph-theoretic analysis, Hausdorff distance, and neural network classification for enhanced biometric security.

Findings

Provides a feature space representation of minutiae

Establishes a lower bound on distinguishable fingerprints

Uses neural networks for fingerprint classification

Abstract

This paper presents an effective method for fingerprint verification based on a data mining technique called minutiae clustering and a graph-theoretic approach to analyze the process of fingerprint comparison to give a feature space representation of minutiae and to produce a lower bound on the number of detectably distinct fingerprints. The method also proving the invariance of each individual fingerprint by using both the topological behavior of the minutiae graph and also using a distance measure called Hausdorff distance.The method provides a graph based index generation mechanism of fingerprint biometric data. The self-organizing map neural network is also used for classifying the fingerprints.