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The Scientific World Journal
Volume 2014 (2014), Article ID 670934, 21 pages
http://dx.doi.org/10.1155/2014/670934
Research Article

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3College of Software, Nanchang Hangkong University, Nanchang 330063, China
4College of Physics and Electronic Information, Wenzhou University, Zhejiang 325035, China

Received 29 August 2013; Accepted 15 December 2013; Published 12 February 2014

Academic Editors: M. Omid and L. Sanchez

Copyright © 2014 Ying Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Ying Chen, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, and Yutong Pang, “Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion,” The Scientific World Journal, vol. 2014, Article ID 670934, 21 pages, 2014. doi:10.1155/2014/670934