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An experimental study of relative total variation and probabilistic collaborative representation for iris recognition

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Abstract

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.

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Acknowledgments

The authors thank the anonymous reviewers for their thorough and valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (NSFC) Grant No. 61871279, the Industrial Cluster Collaborative Innovation Project of Chengdu Grant No. 2016-XT00-00015-GX, the Sichuan Science and Technology Program Grant No. 2018HH0143 and the Sichuan Education Department Program Grant No. 18ZB0355.

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Authors and Affiliations

  1. Image Information Institute, College of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, People’s Republic of China

    Pradeep Karn, XiaoHai He, Jin Zhang & Yanteng Zhang

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  1. Pradeep Karn
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  2. XiaoHai He
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  3. Jin Zhang
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Correspondence to Pradeep Karn.

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Karn, P., He, X., Zhang, J. et al. An experimental study of relative total variation and probabilistic collaborative representation for iris recognition. Multimed Tools Appl 79, 31783–31801 (2020). https://doi.org/10.1007/s11042-020-09553-7

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