A Multiple Indexes Quality Assessment for Fingerprint
Sheng Chang, Qijun Huang, Hao Wang, Jin He
Available Online August 2015.
- 10.2991/meita-15.2015.112How to use a DOI?
- Quality Estimation; Support Vector Machine; Wavelet Transform; Harris Corner
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).
- © 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/).
Cite this article
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 -