Abstract
The problem of choosing the best biometric template from a group constructed based on a sequence of registered images is addressed. A method based on the analysis of the matrix of distances of a group of templates is proposed. A comparison with the standard approaches using the quality indices of the initial image is performed. It is specified that the quality indices of the image are developed for the problem of rejecting poor images and little suit the problem of choosing the best image. The computer experiments were conducted based on several databases available in open source with more than 70000 images. The tests have shown that the proposed method provides a slightly better quality of the chosen templates. Note that the method does not require the development of additional quality measures and employs the calculations of the distance available.
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References
K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, Handbook of Iris Recognition (Springer, Heidelberg, 2012).
International Biometric Group, Comparative Biometric Testing, Round 7, Public Report (2009). http://l.b5z.net/i/u/6084428/i/CBT7_IBGReport.pdf
K. A. Gan”kin, A. V. Gneushev, and I. A. Matveev, “Iris image segmentation Based on approximate methods with subsequent refinements,” J. Comput. Syst. Sci. Int. 53, 224 (2014).
X.-D. Zhu, Y.-N. Liu, X. Ming, and Q.-L. Cui, “A quality evaluation method of iris images Sequence based on wavelet coefficients in “Region of Interest”,” in Proceedings of the 4th International Conference on Computer and Information Technology (Wuhan, China, 2004), pp. 24–27.
J. K. Pillai, V. M. Patel, R. Chellappa, and N. K. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Machine Intelligence 3, 1877–1893 (2011).
H. Ren, H. Yuqing, J. Pan, and L. Li, “Super resolution reconstruction and recognition for iris image sequence biometric recognition,” Lect. Notes Comp. Sci. 7701, 193–201 (2012).
J. Galbally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia, “Iris liveness detection based on quality related features,” in Proceedings of the 5th International Conference of International Association for Pattern Recognition (IAPR) on Biometrics (New Delhi, India, 2012), p. 271.
A. L. Bandi, V. P. Thotakura, and S. Ravi, “Identity recognizing using iris scan with multiple frames of video,” Int. J. Comput. Sci. Eng. 4(3), 388–391 (2012).
K. P. Hollingsworth, K. W. Bowyer, and P. J. Flynn, “Image averaging for improved iris recognition,” in Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics (Alghero, Italy, 2009), pp. 1112–1121.
N. K. Mahadeo, A. P. Paplinski, and S. Ray, “Optimization of iris codes for improved recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Columbus, Ohio, USA, 2014), pp. 48–55.
V. I. Tsurkov, “An analytical model of edge protection under noise suppression by anisotropic diffusion,” J. Comput. Syst. Sci. Int. 39, 437 (2000).
National Institute of Standards and Technology, Iris Quality Calibration and Evaluation. http://www.nist.gov/itl/iad/ig/irexii.cfm
N. Feddaoui, H. Mahersia, and K. Hamrouni, “Improving iris recognition performance using quality measures,” in Advanced Biometric Technologies, Ed. by G. Chetty and J. Yang (InTech, 2011), Ch. 12.
N. D. Kalka, V. Dorairaj, Y. N. Shah, N. A. Schmid, and B. Cukic, “Image quality assessment for iris biometric,” in Proceedings of the 24th Annual Meeting of the Gesellschaft fur Klassification (Passau, Germany, 1999), pp. 445–452.
M. Vatsa, R. Singh, and A. Noore, “Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing,” IEEE Trans. Syst. Man, Cybernet. B: Cybernet. 38(4), 1021–1035 (2008).
J. Zuo and N. A. Schmid, “An automatic algorithm for evaluating the precision of iris segmentation,” in Proceedings of the 2nd International Conference on Biometrics: Theory, Applications and Systems (Arlington, VA, USA, 2008).
A. A. Mironov and V. I. Tsurkov, “Class of distributed problems with a minimax criterion,” Dokl. Akad. Nauk 336, 35 (1994).
A. P. Tizik and V. I. Tsurkov, “Iterative functional modification method for solving a transportation problem,” Autom. Remote Control 73, 134 (2012).
A. A. Mironov and V. I. Tsurkov, “Transportation problems with a minimax criterion,” Dokl. Math. 53, 119 (1996).
A. A. Mironov and V. I. Tsurkov, “Minimax under nonlinear transportation constraints,” Dokl. Math. 64, 351 (2001).
A. A. Mironov and V. I. Tsurkov, “Open transportation models with a minimax criterion,” Dokl. Math. 64, 374 (2001).
University of Bath, Iris Image Database (2005). http://www.bath.ac.uk/elec-eng/research/sipg/irisweb/
Chinese Academy of Sciences Institute of Automation, Iris Image Database, Version 3 (2005). http://www.cbsr.ia.ac.cn/IrisDatabase.htm
National Institute of Standards and Technology. Iris Challenge Evaluation. http://iris.nist.gov/ice/
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Original Russian Text © A.N. Gneushev, D.V. Kovkov, I.A. Matveev, V.P. Novik, 2015, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2015, No. 3, pp. 72–78.
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Gneushev, A.N., Kovkov, D.V., Matveev, I.A. et al. Optimizing the selection of a biometric template from a sequence. J. Comput. Syst. Sci. Int. 54, 399–405 (2015). https://doi.org/10.1134/S1064230715030077
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DOI: https://doi.org/10.1134/S1064230715030077