Abstract
In order to select as many available irises as possible for recognition through the same indicators, a quality evaluation algorithm for constrained sequence iris is proposed in this paper. In the case where other indicators are set idealization, a variety of iris quality indicators are set from the perspective of sharpness, iris region nature, and offset degree. According to the causal relationship among quality indicators, the order of indicators evaluation will be adjusted, and then a quality decision reasoning process can be formed. The results of experiments used the JLU iris library of Jilin University indicate that the algorithm can effectively improve the survival rate of available iris in the sequence iris and play an active role in improving iris recognition accuracy.
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Acknowledgments
The authors would like to thank the referee’s advice and acknowledge the support of the National Natural Science Foundation of China (NSFC) under Grant No. 61471181. Jilin Province Industrial Innovation Special Fund Project under Grant No. 2019C053-2, 2019C053-6. Science and technology project of the Jilin Provincial Education Department under Grant No. JJKH20180448KJ. Thanks to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this project.
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Shuai, L. et al. (2019). Constrained Sequence Iris Quality Evaluation Based on Causal Relationship Decision Reasoning. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_38
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DOI: https://doi.org/10.1007/978-3-030-31456-9_38
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