A Fingerprint-based Access Control using Principal Component Analysis and Edge Detection

TL;DR

This paper introduces a new fingerprint verification method combining PCA and edge detection, utilizing a novel feature H derived from distance metrics to improve database relevance decisions.

Contribution

The paper proposes a new feature H based on Euclidean and Mahalanobis distances, enhancing fingerprint image relevance assessment in databases.

Findings

Effective discrimination of fingerprint images using the H feature.

Improved ROC curve performance for fingerprint relevance detection.

Method allows adjustable false positive and false negative rates.

Abstract

This paper presents a novel approach for deciding on the appropriateness or not of an acquired fingerprint image into a given database. The process begins with the assembly of a training base in an image space constructed by combining Principal Component Analysis (PCA) and edge detection. Then, the parameter H, a new feature that helps in the decision making about the relevance of a fingerprint image in databases, is derived from a relationship between Euclidean and Mahalanobian distances. This procedure ends with the lifting of the curve of the Receiver Operating Characteristic (ROC), where the thresholds defined on the parameter H are chosen according to the acceptable rates of false positives and false negatives.