A framework of multitemplate ensemble for fingerprint verification

Abstract

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

A Template Generation and Improvement Approach for Finger-Vein Recognition

“…Current template generation methods [16][17][18][19][20][21] aim at improving performance, mainly verification error rates. Therefore, the biometric template generation should target its minimization instead of being based on subjective human perception of enrollment template quality.…”

“…In recent years, various methods have been employed for fingerprints to reduce intra-class variations by fusing multiple enrollment impressions. Some works [16][17][18][19][20][21] are proposed to combine multiple enrolled impressions for improvement of the system's performance. For example, in works [16][17][18][19], several mathematical models are employed to select prototype fingerprint templates for a finger from a given set of fingerprint impressions and then merge the matching scores between a test image and several templates for verification.…”

“…Some works [16][17][18][19][20][21] are proposed to combine multiple enrolled impressions for improvement of the system's performance. For example, in works [16][17][18][19], several mathematical models are employed to select prototype fingerprint templates for a finger from a given set of fingerprint impressions and then merge the matching scores between a test image and several templates for verification. To improve verification accuracy, the works [20,21] are proposed to match a given impression with respect to each of the enrollment impressions, and then robust fusion strategies such as feature level and decision level have been used to combine the individual matching results for verification.…”

Fingerprint Identification Based on Semi-supervised FSS

“…Yin. et al [11] proposed a template update method to better represent a given finger. MDIST and Yin's method have better performance than other template update methods [3], [11].…”