Abstract
In this work we propose an alternative image representation model to efficiently characterize iris textures based on the Hermite transform. The Hermite transform can simulate some properties of the mammalian visual system and it is founded on a well established mathematical framework. These properties are used to extract the most important information of the iris textures. The results show that the Hermite transform is able to characterize iris textures as well as the Gabor model, with the advantage on the second that the discrete analysis filters in the Hermite transform are given by the Krawtchouk polynomials and, it is not needed to compute the filter coefficients by means of optimization methods, nor to suppress the zero mean (d.c. response). The proposed iris recognition system achieved an overall performance of 97.34% and a Correct Access Rate (CAR) of 90.29% when the False Access Rate (FAR) was closed to zero.
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Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal 23(2), 61–70 (2001)
Poursaberi, A., Araabi, B.N.: A half-eye wavelet based method for iris recognition. In: 5th International Conference on Intelligent Systems Design and Applications, pp. 262–267. IEEE Computer Society, Washington (2005)
Daugman, J.G.: Uncertainty relation for resolution in space, spatialfrequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1160–1169 (1985)
Bovik, A.C., Clark, M., Geisler, W.S.: Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 55–73 (1990)
Clausi, D.A., Ed Jernigan, M.: Designing Gabor filters for optimal texture separability. Pattern Recognition 33(11), 1835–1849 (2000)
Martens, J.B.: The Hermite transform-theory. IEEE Transactions on Acoustics, Speech, and Signal Processing 38(9), 1595–1606 (1990)
Young, R.A.: Orthogonal basis functions for form vision derived from eigenvector analysis. In: ARVO Abstracts, p. 22. Association for Research in Vision and Ophthalmology, Sarasota (1978)
Silvan-Cardenas, J.L., Escalante-Ramirez, B.: The multiscale Hermite transform for local orientation analysis. IEEE Transactions on Image Processing 15(5), 1236–1253 (2006)
Rivero-Moreno, C.J., Bres, S.: Texture feature extraction and indexing by Hermite filters. In: 17th International Conference on Pattern Recognition, vol. 1, pp. 684–687. IEEE Computer Society, Washington (2004)
CASIAv1.0 Iris Image Database, Chinese Academy of Sciences, Institute of Automation, http://www.cbsr.ia.ac.cn/IrisDatabase.htm
Canny, F.J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Chan, R., Siu, W.C.: A new approach for efficient Hough transform for circles. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 99–102 (1989)
Michaelis, M., Sommer, G.: Basic functions for early vision. Technical Report Bericht 9413, Institut für Informatik und Praktische Mathematik Christian-Albrechts-Universität zu Kiel (August 1994)
Daugman, J.G.: New methods in iris recognition. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(5), 1167–1175 (2007)
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Estudillo-Romero, A., Escalante-Ramirez, B. (2008). The Hermite Transform: An Alternative Image Representation Model for Iris Recognition. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_11
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DOI: https://doi.org/10.1007/978-3-540-85920-8_11
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