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Iris Segmentation Using Feature Channel Optimization for Noisy Environments

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Abstract

In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.

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Notes

  1. The code is available at https://github.com/wowotou1022/IrisSegment.

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Acknowledgments

The authors are grateful to the reviewers for their valuable comments.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants U1736120, 61525203, U1636206, U1936214 and Natural Science Foundation of Shanghai under Grant 19ZR1419000.

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Authors and Affiliations

  1. Shanghai Institute for Advanced Communication and Data Science and School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Kangli Hao, Guorui Feng, Yanli Ren & Xinpeng Zhang

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  1. Kangli Hao
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  2. Guorui Feng
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  3. Yanli Ren
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  4. Xinpeng Zhang
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Correspondence to Guorui Feng.

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Hao, K., Feng, G., Ren, Y. et al. Iris Segmentation Using Feature Channel Optimization for Noisy Environments. Cogn Comput 12, 1205–1216 (2020). https://doi.org/10.1007/s12559-020-09759-9

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