3 November 2014 Iris recognition based on robust principal component analysis
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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.
© 2014 SPIE and IS&T
Pradeep Karn, Xiaohai He, Shuai Yang, Xiaohong Wu, "Iris recognition based on robust principal component analysis," Journal of Electronic Imaging 23(6), 063002 (3 November 2014). https://doi.org/10.1117/1.JEI.23.6.063002 . Submission:

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