Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors

Zhiqiang HU  Dongju LI  Tsuyoshi ISSHIKI  Hiroaki KUNIEDA  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.6   pp.1290-1302
Publication Date: 2017/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7401
Type of Manuscript: PAPER
Category: Pattern Recognition
narrow swipe sensor,  fuzzy c-means,  fingerprint verification,  minutiae descriptor,  fingerprint template improvement,  

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Narrow swipe sensor has been widely used in embedded systems such as smart-phone. However, the size of captured image is much smaller than that obtained by the traditional area sensor. Therefore, the limited template coverage is the performance bottleneck of such kind of systems. Aiming to increase the geometry coverage of templates, a novel fingerprint template feature synthesis scheme is proposed in the present study. This method could synthesis multiple input fingerprints into a wider template by clustering the minutiae descriptors. The proposed method consists of two modules. Firstly, a user behavior-based Registration Pattern Inspection (RPI) algorithm is proposed to select the qualified candidates. Secondly, an iterative clustering algorithm Modified Fuzzy C-Means (MFCM) is proposed to process the large amount of minutiae descriptors and then generate the final template. Experiments conducted over swipe fingerprint database validate that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).