A Multiple Indexes Quality Assessment for Fingerprint

DOI

10.2991/meita-15.2015.112How to use a DOI?

Keywords

Quality Estimation; Support Vector Machine; Wavelet Transform; Harris Corner

Abstract

The performance of fingerprint recognition relies heavily on the quality of fingerprint images. In this paper, a multiple indexes fingerprint quality assessment is proposed for poor fingerprints. This method fuses seven indexes from three kinds of typical fingerprint features (gray features, local features and global features) by a support vector machine classifier. Experiment results on FVC2004 database show that our proposed method can identify poor quality fingerprint accurately (94.3% -98.7% for different sub databases).

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Sheng Chang
AU  - Qijun Huang
AU  - Hao Wang
AU  - Jin He
PY  - 2015/08
DA  - 2015/08
TI  - A Multiple Indexes Quality Assessment for Fingerprint
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 614
EP  - 618
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.112
DO  - 10.2991/meita-15.2015.112
ID  - Chang2015/08
ER  -