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Journal of Electronic Imaging

Iris recognition based on robust principal component analysis
Author(s): Pradeep Karn; Xiaohai He; Shuai Yang; Xiaohong Wu
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Paper Abstract

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Paper Details

Date Published: 3 November 2014
PDF: 8 pages
J. Electron. Imaging. 23(6) 063002 doi: 10.1117/1.JEI.23.6.063002
Published in: Journal of Electronic Imaging Volume 23, Issue 6
Show Author Affiliations
Pradeep Karn, Sichuan Univ. (China)
Xiaohai He, Sichuan Univ. (China)
Shuai Yang, Sichuan Univ. (China)
Xiaohong Wu, Sichuan Univ. (China)


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