IEICE TRANSACTIONS on Information
Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors
Summary :
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).
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.6 pp.1290-1302
- Publication Date
- 2017/06/01
- Publicized
- 2017/03/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7401
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Zhiqiang HU
Tokyo Institute of Technology
Dongju LI
Tokyo Institute of Technology
Tsuyoshi ISSHIKI
Tokyo Institute of Technology
Hiroaki KUNIEDA
Tokyo Institute of Technology
Keyword
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Zhiqiang HU, Dongju LI, Tsuyoshi ISSHIKI, Hiroaki KUNIEDA, "Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 6, pp. 1290-1302, June 2017, doi: 10.1587/transinf.2016EDP7401.
Abstract: 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).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7401/_p
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@ARTICLE{e100-d_6_1290,
author={Zhiqiang HU, Dongju LI, Tsuyoshi ISSHIKI, Hiroaki KUNIEDA, },
journal={IEICE TRANSACTIONS on Information},
title={Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors},
year={2017},
volume={E100-D},
number={6},
pages={1290-1302},
abstract={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).},
keywords={},
doi={10.1587/transinf.2016EDP7401},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Narrow Fingerprint Template Synthesis by Clustering Minutiae Descriptors
T2 - IEICE TRANSACTIONS on Information
SP - 1290
EP - 1302
AU - Zhiqiang HU
AU - Dongju LI
AU - Tsuyoshi ISSHIKI
AU - Hiroaki KUNIEDA
PY - 2017
DO - 10.1587/transinf.2016EDP7401
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2017
AB - 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).
ER -
English
