Abstract
Iris recognition is widely accepted in different kinds of applications. When it comes to mobile iris recognition, the task is quite challenging because of the low quality of iris images. To solve this problem, we propose a mobile iris recognition algorithm based on fusing features and Joint Bayesian. The iris feature representations are extracted by 2D Gabor and Ordinal Measures. Then these feature representations are fused by Joint Bayesian and the similarity of two iris images is measured by log-likelihood ratio. The experiments are conducted on MIR-Train database and a self-established low-quality iris image database (LQIID). The proposed method achieves EER at 1.2% on MIR-Train database and 0.8% on LQIID. These experiments support the effectiveness of the proposed method.
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References
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Daugman, J.: How iris recognition works. IEEE Trans. Circ. Syst. Video Technol. 14(1), 21–30 (2004)
Wildes, R.P., Asmuth, J.C., Green, G.L., Hsu, S.C., Kolczynski, R.J., Matey, J.R., McBride, S.E.: A machine-vision system for iris recognition. Mach. Vis. Appl. 9(1), 1–8 (1996)
Boles, W.W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185–1188 (1998)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003)
Sun, Z., Wang, Y., Tan, T., Cu, J.: Robust direction estimation of gradient vector field for iris recognition. In: International Conference on Pattern Recognition, pp. 783–786 (2004)
Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211–2226 (2009)
Desoky, A.I., Ali, H.A., Abdel-Hamid, N.B.: Enhancing iris recognition system performance using templates fusion. Ain Shams Eng. J. 3(2), 133–140 (2012)
Li, C., Zhou, W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1–11 (2014)
Tan, T., Zhang, X., Sun, Z., Zhang, H.: Noisy iris image matching by using multiple cues. Pattern Recognit. Lett. 33(8), 970–977 (2012)
Wang, Q., Zhang, X., Li, M., Dong, X., Zhou, Q., Yin, Y.: Adaboost and multi-orientation 2D gabor-based noisy iris recognition. Pattern Recognit. Lett. 33(8), 978–983 (2012)
Santos, G., Hoyle, E.: A fusion approach to unconstrained iris recognition. Pattern Recognit. Lett. 33(8), 984–990 (2012)
Shin, K.Y., Nam, G.P., Jeong, D.S., Cho, D.H., Kang, B.J., Park, K.R., Kim, J.: New iris recognition method for noisy iris images. Pattern Recognit. Lett. 33(8), 991–999 (2012)
Li, P., Liu, X., Zhao, N.: Weighted co-occurrence phase histogram for iris recognition. Pattern Recognit. Lett. 33(8), 1000–1005 (2012)
Marsico, M.D., Nappi, M., Riccio, D.: Noisy iris recognition integrated scheme. Pattern Recognit. Lett. 33(8), 1006–1011 (2012)
Li, P., Ma, H.: Iris recognition in non-ideal imaging conditions. Pattern Recognit. Lett. 33(8), 1012–1018 (2012)
Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., Napieralski, A.: A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognit. Lett. 33(8), 1019–1026 (2012)
Institute of Automation, Chinese Academy of Sciences: CASIA iris database. http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
Institute of Automation, Chinese Academy of Sciences: CASIA iris database. http://biometrics.idealtest.org/2016/MIR2016.jsp
Zhang, Q., Li, H., Sun, Z., He, Z., Tan, T.: Exploring complementary features for iris recognition on mobile devices. In: International Conference on Biometrics, pp. 1–8 (2016)
Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 959–973 (2003)
Daugman, J.G.: Complete discrete 2-D gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoust. Speech Signal Process. 36(7), 1169–1179 (1988)
Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012 Part III. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_41
Zhang, M., Zhang, Q., Sun, Z., Zhou, S., Ahmed, N.U.: The BTAS* competition on mobile iris recognition. In: IEEE International Conference on Biometrics Theory, Applications and Systems, pp. 1–7 (2016)
Acknowledgement
This research is partly supported by “the Fundamental Research Funds for the Central Universities”, N160503003 and “National Natural Science Foundation of China”, 61703088. The authors would like to thank “National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA)” for their great contributions in sharing iris image databases.
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Wang, Q., Su, X., Cai, Z., Zhang, X. (2017). Mobile Iris Recognition via Fusing Different Kinds of Features. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_43
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DOI: https://doi.org/10.1007/978-3-319-69923-3_43
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