An experimental study of relative total variation and probabilistic collaborative representation for iris recognition
| Autor: | Jin Zhang, Xiaohai He, Pradeep Karn, Yanteng Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
Pixel
Computer Networks and Communications Computer science business.industry Motion blur Iris recognition Probabilistic logic Pattern recognition Regularization (mathematics) Image (mathematics) medicine.anatomical_structure Hardware and Architecture Media Technology medicine Artificial intelligence Iris (anatomy) Representation (mathematics) business Software |
| Zdroj: | Multimedia Tools and Applications. 79:31783-31801 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | Iris images collected under different conditions often suffer from specular reflections, cast shadows, motion blur, defocus blur, occlusion caused by eyelashes and eyelids, eyeglasses, hair and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, an iris recognition method based on relative total variation (RTV) and probabilistic collaborative representation is proposed. RTV uses the l1 norm regularization method to robustly suppress noisy pixels to achieve accurate iris localization, while probability collaborative representation maximizes the probability that the test sample belongs to each of the multiple classes. The final recognition rate is calculated based on the class having maximum probability. Experimental results using CASIA-V4-Lamp and IIT-Delhi V1iris image databases showed that the proposed method achieved competitive performance in both recognition accuracy and computational efficiency. |
| Databáze: | OpenAIRE |
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