Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors

Abstract

SUMMARYNarrow 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). key words: narrow swipe sensor, fuzzy c-means, fingerprint verification, minutiae descriptor, fingerprint template improvement

A Template Generation and Improvement Approach for Finger-Vein Recognition

“…In this section, a template generation approach is proposed for finger-vein verification. To reduce intra-class variations, similar to existing approaches [22][23][24][25], we merge multiply templates from same class to generate a "super-template" and then improve it by using the vein features in finger-vein images captured at different time. The template generation aims at reducing the intra-class variations, so a "super-template" is directly generated by minimizing the intra-class distance.…”

“…To overcome this problem, a "super-template" is generated by combining multiple capturing impressions together in works [22][23][24][25]. The "super-template" is considered as a single template which contains highly likely true minutia based on multiple fingerprint images.…”

“…The non-linkability property prevents cross-matching across different applications, thereby preserving the privacy of the individual. Different from security template generation, the enrollment template generation and improvement in works [22][23][24][25][26][27][28] is to improve recognition accuracy and its advantages are concluded as: (1) In registration phase, multiple enrollment images corresponding to each user may be stored. Matching performed among multiple templates achieves the high memory and computation consumption.…”

“…Based on this assumption, we convert the enrollment template generation and improvement into two optimization problems. Unlike existing works [22][23][24][25], our approaches directly target minimization of intra-class variations or reduction of verification errors instead of subjective human perception of enrollment template quality. Second, an automatic template generation approach is proposed for finger-vein verification.…”