Fingerprint Matching Using Minutiae-Singular Points Network

Abstract

Biometrics have featured prominently for human verification and identification with fingerprint emerging as the dominant one. The dominance of fingerprint has been established by the continuous emergence of different forms of Automated Fingerprint Identification Systems (AFIS). In the course of performing human verification and identification, an AFIS performs fingerprint enrolment, enhancement, minutiae extraction and pattern matching. One of the challenges confronting fingerprint pattern matching is variation in image ridge orientation which often results in mismatch among images from the same source. In this paper, an algorithm for fingerprint pattern matching that addresses this problem is proposed. The algorithm uses the Euclidian and spatial relationships between the minutiae and singular points to determine the pattern matching scores for fingerprint images. Experimental study on FVC2002 fingerprint database measured the False Acceptance Rate (FAR), False Rejection Rate (FRR), Receiver Operating Characteristics (ROC) Curve, Equal Error Rate (EER) and the Average Matching Time (AMT). Analyses of the metrics obtained from the measurements revealed high adequacy level of the new algorithm at distinguishing fingerprints obtained from different sources. It is also revealed that correct matching of images from same source is heavily dependent on the quality of the images.