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SIFT based iris recognition with normalization and enhancement

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Abstract

SIFT is a novel and promising method for iris recognition. However, some shortages exist in many related methods, such as difficulty of feature extraction, feature loss, and noise point introduction. In this paper, a new method named SIFT-based iris recognition with normalization and enhancement is proposed for achieving better performance. In Comparison with other SIFT-based iris recognition algorithms, the proposed method can overcome the difficulties of extreme point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and enhancement steps are crucial for SIFT-based iris recognition, and the proposed method can achieve satisfactory recognition performance.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61173069 and 61070097, and Shandong Province Higher Educational Science and Technology Program under Grant No. J11LG28. The authors would like to thank Wei Qin and Shuaiqiang Wang for their helpful comments and constructive advices on structuring the paper.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, People’s Republic of China

    Gongping Yang, Shaohua Pang, Yilong Yin, Yanan Li & Xuzhou Li

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  1. Gongping Yang
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  2. Shaohua Pang
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  3. Yilong Yin
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  4. Yanan Li
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  5. Xuzhou Li
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Correspondence to Yilong Yin.

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Yang, G., Pang, S., Yin, Y. et al. SIFT based iris recognition with normalization and enhancement. Int. J. Mach. Learn. & Cyber. 4, 401–407 (2013). https://doi.org/10.1007/s13042-012-0101-0

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  • DOI: https://doi.org/10.1007/s13042-012-0101-0

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