Abstract
Due to the advantages in uniqueness, convenience and non-contact, iris recognition is widely deployed for automatic identity authentication. Instead of a single signature, multiple templates are registered in real-world applications for the diversity of gallery samples, resulting in great enhanced user experience. In this paper, we exploit the connection among the multiple registration data and then make efforts to give a more comprehensive decision based on them. A novel hybrid fusion framework is proposed to fuse information at groups in feature and score levels. Specifically, the gallery samples are firstly divided into groups to balance the abundance and the robustness of information. Afterwards, hierarchical fusion is performed at the groups, which is actually the procedure of information mapping and reducing. The experimental results demonstrate the effectiveness and generalization ability of the proposed hybrid fusion framework.
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Li, H., Sun, Z., Zhang, M., Wang, L., Xiao, L., Tan, T.: A brief survey on recent progress in iris recognition. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds.) CCBR 2014. LNCS, vol. 8833, pp. 288–300. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12484-1_33
Hollingsworth, K., Bowyer, K., Flynn, P.: All iris code bits are not created equal. In: IEEE International Conference on Biometrics Theory, Applications, and Systems 2007, pp. 1–6. IEEE Press (2007)
Nguyen, K., Fookes, C., Sridharan, S.: Robust mean super-resolution for less cooperative NIR iris recognition at a distance and on the move. In: Symposium on Information and Communication Technology, SOICT 2010, pp. 122–127. Symposium on Information & Communication Technology Press, Hanoi (2010)
Grover, J., Hanmandlu, M.: Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger-knuckle-print based authentication. J. Appl. Soft Comput. 31, 1–13 (2015)
Madane, M., Sudeep Thepade, D.: Score level fusion based bimodal biometric identification using Thepade’s sorted n-ary block truncation coding with variod proportions of iris and palmprint traits. J. Proc. Comput. Sci. 79, 466–473 (2016)
Hanmandlu, M., Grover, J., Gureja, A., Gupta, H.: Score level fusion of multimodal biometrics using triangular norms. J. Pattern Recogn. Lett. 32, 1843–1850 (2011)
He, M., et al.: Performance evaluation of score level fusion in multimodal biometric systems. J. Pattern Recogn. 43, 1789–1800 (2010)
Sim, H., Asmuni, H., Hassan, R., Othman, R.: Multimodal biometrics: weighted score level fusion based on non-ideal iris and face images. J. Expert Syst. Appl. 41, 5390–5404 (2014)
Miao, D., Zhang, M., Sun, Z., Tan, T., He, Z.: Bin-based classifier fusion of iris and face biometrics. J. Neurocomput. 224, 105–118 (2017)
Tao, Q., Veldhuis, R.: Threshold-optimized decision-level fusion and its application to biometrics. J. Pattern Recogn. 42, 823–836 (2009)
Dong, W., Sun, Z., Tan, T.: Iris matching based on personalized weight map. J IEEE Trans. Pattern Anal. Mach. Intell. 33, 1744–1757 (2011)
Liu, N., Liu, J., Sun, Z., Tan, T.: A code-level approach to heterogeneous iris recognition. J. IEEE Trans. Inf. Forensics Secur. 12, 2373–2386 (2017)
Sun, Z., Tan, T.: Ordinal measures for iris recognition. J IEEE Trans. Pattern Anal. Mach. Intell. 31, 2211–2226 (2009)
CASIA-Dataset. http://biometrics.idealtest.org/
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This work is supported by the Natural Science Foundation of China (61503365).
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Zhang, H. et al. (2018). Hybrid Fusion Framework for Iris Recognition Systems. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_50
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DOI: https://doi.org/10.1007/978-3-319-97909-0_50
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