Advancing Fingerprint Template Generation and Matching With Recast Minutiae Clustering and mRBFN

Abstract

Rapid development of automation in the day-to-day life activity marks up the need of securing bio-metric template and the privacy of rightful owner. Minutiae-based matching is the most popular in the fingerprint recognition system, which greatly suffers from non-linear distortion like translation and rotation. To deal with linear distortion most of the technique proposed in the literature depends upon a reference or singular point. The paper proposes a binary template generation technique which applies an unsupervised clustering technique without fixing the number of clusters. Instead of position and orientation of the minutiae points the cardinality of the clusters are stored and converted into binary template. No spatial pattern information about the fingerprint is stored in the template to protect it from spoofing and information leakage. By the help of modified Radial Basis Function Network (mRBFN) with robust and efficient matching technique the generated templates are matched for authentication. We use MCYT dataset for training the mRBFN. The efficiency of the proposed scheme is evaluated on FVC 2000, FVC 2002 and FVC 2004 dataset.