Abstract
Excellent recognition accuracies have been reported when using iris images, particularly when high-quality iris images can be acquired. The best-known strategy for matching iris images requires segmenting the iris from the background, converting the segmented iris image from Cartesian coordinates to polar coordinates, using Gabor wavelets to obtain a binary code to represent that iris, and using the Hamming distances between such binary representations to determine whether two iris images match or do not match. However, some of the component operations may not work well when the iris images are of poor quality, perhaps as a result of the long distance between the camera and the subject. One approach to matching images with appearance variations is the use of correlation filters (CF). In this chapter, we discuss the use of CFs for iris recognition. CFs exhibit important benefits such as shift-invariance and graceful degradation and have proven worthy of consideration in other pattern recognition applications such as automatic target recognition. In this chapter, we will discuss the basics of CF design and show how CFs can be used for iris segmentation and matching.
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References
Wildes, R.: Chapter 3: Iris recognition. In: Wayman, J., Jain, A., Maltoni, D., Maio, D. (eds.) Biometric Systems, pp. 63–95. Springer, Berlin (2005)
Daugman, J.: Probing the uniqueness and randomness of IrisCodes: results from 200 billion iris pair comparisons. Proc. IEEE 94(11), 1927–1935 (2006)
Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Vijaya Kumar, B.V.K., Xie, C., Thornton, J.: Iris verification using correlation filters. In: Proceedings of 4th International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA). LCNS 2688, pp. 697–705. Springer, Berlin/Heidelberg (2003)
Vijaya Kumar, B.V.K., Mahalanobis, A., Juday, R.: Correlation Pattern Recognition. Cambridge University Press, Cambridge (2005)
Mahalanobis, A., Ortiz, L., Vijaya Kumar, B.V.K.: Performance of the MACH/DCCF algorithms on the 10-class public release MSTAR data set. Proc. SPIE 3721, 285–291 (1999)
Vijaya Kumar, B.V.K., Savvides, M., Xie, C.: Correlation pattern recognition for face recognition. Proc. IEEE 94(11), 1963–1976 (2006)
Refregier, P.: Filter design for optical pattern recognition: multicriteria optimization approach. Opt. Lett. 15(15), 854–856 (1990)
Mahalanobis, A., Vijaya Kumar, B.V.K., Casasent, D.: Minimum average correlation energy filters. Appl. Opt. 26, 3630–3633 (1987)
Vijaya Kumar, B.V.K.: Minimum variance synthetic discriminant functions. JOSA-A 3, 1579–1584 (1986)
Duda, R.D., Hart, P.E.: Use of the Hough transform to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)
Thornton, J.: Iris pattern matching: a probabilistic model based on discriminative cues. Ph.D. dissertation, Carnegie Mellon University (2007)
Thornton, J., Savvides, M., Vijaya Kumar, B.V.K.: Robust Iris recognition using advanced correlation techniques. In: Proceedings of International Conference on Image Analysis and Recognition (ICIAR), Image Analysis and Recognition, Lecture Notes in Computer Science #3656, pp. 1098–1105. Springer Berlin/Heidelberg (2005)
http://www.nist.gov/itl/iad/ig/focs.cfm, Face and ocular challenge series (FOCS).
Thornton, J., Savvides, M., Vijaya Kumar, B.V.K.: A unified Bayesian approach to deformed pattern matching of iris images. IEEE Trans. Pattern Anal. Mach. Intell. 29, 596–606 (2007)
Frey, B.J.: Graphical Models for Machine Learning and Digital Communication. MIT Press, Cambridge, MA (1998)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Ana. Mach. Intell. 6(6), 721–741 (1984)
Jordan, M.I.: Learning in Graphical Models. MIT Press, Cambridge, MA (1999)
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Kumar, B.V.K., Thornton, J., Savvides, M., Boddeti, V.N., Smereka, J.M. (2013). Application of Correlation Filters for Iris Recognition. In: Burge, M., Bowyer, K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4402-1_17
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DOI: https://doi.org/10.1007/978-1-4471-4402-1_17
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