Hybrid Minutiae-based Architecture for Automated Fingerprint Verification System

Fingerprints are patterns formed on the epidermis of fingertip and they are characterized with minutiae and their overall ridge flow patterns. In this paper, fingerprint minutiae are considered to have both the quantitative and qualitative properties which could help to ensure accurate verification of fingerprint if properly utilized. The hybrid architecture of minutiae-based verification is in effect a model that caters for enhancement in terms of minutiae quantity and quality on fingerprint. While researches have proven that good quality of fingerprint minutiae can guarantee accurate verification of the fingerprint, false acceptance and rejection rates are still being recorded largely because the improvement associated with the quality of minutiae may not be sufficient to address the problems associated with fingerprint during sensing. Nevertheless, an improvement in the number (i.e. quantity) of minutiae extracted from fingerprint could be useful in many instances. Therefore, this paper introduces a dimension whereby necessary and sufficient condition is set for the selection of quantity of minutiae needed for verification. This approach is designed to complement existing minutiae quality enhancement approach aimed at achieving accurate verification in Automated Fingerprint Verification System (AFVS). Hence, hybrid architecture of minutiae-based fingerprint verification is presented based on the data reduction principle of data mining.