Iris recognition based on robust principal component analysis
| Autor: | Pradeep Karn, Xiao Hai He, Xiao Hong Wu, Shuai Yang |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Biometrics
Image quality Computer science business.industry Feature extraction Iris recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation Atomic and Molecular Physics and Optics Computer Science Applications Principal component analysis IRIS (biosensor) Computer vision Artificial intelligence Electrical and Electronic Engineering business Robust principal component analysis |
| Zdroj: | Journal of Electronic Imaging. 23:063002 |
| ISSN: | 1017-9909 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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