How to improve performance of an automatic fingerprint verification system (AFVS) is always a big challenge in\r\nbiometric verification field. Recently, it becomes popular to improve the performance of AFVS using ensemble\r\nlearning approach to fuse related information of fingerprints. In this article, we propose a novel framework of\r\nfingerprint verification which is based on the multitemplate ensemble method. This framework is consisted of\r\nthree stages. In the first stage, enrollment stage, we adopt an effective template selection method to select those\r\nfingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple\r\ntemplate fingerprints and a virtual centroid of the polyhedron is given. In the second stage, verification stage, we\r\nmeasure the distance between the centroid of the polyhedron and a query image. In the final stage, a fusion rule\r\nis used to choose a proper distance from a distance set. The experimental results on the FVC2004 database prove\r\nthe improvement on the effectiveness of the new framework in fingerprint verification. With a minutiae-based\r\nmatching method, the average EER of four databases in FVC2004 drops from 10.85 to 0.88, and with a ridge-based\r\nmatching method, the average EER of these four databases also decreases from 14.58 to 2.51.
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