Abstract
Biometric authentication has been considered a model for quantitatively establishing the discriminative power of biometric data. The dichotomy model classifies two biometric samples as coming either from the same person or from two different people. This paper reviews features, distance measures, and classifiers used in iris authentication. For feature extraction we compare simple binary and multi-level 2D wavelet features. For distance measures we examine scalar distances such as Hamming and Euclidean, feature vector and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the histogram distance, and a support vector machine classifier.
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Daugman, J.G.: High confidence visual recognition of persons by a test of statistical inde-pendence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
Cha, S.-H.: Use of Distance Measures in Handwriting Analysis. PhD dissertation, SUNY at buffalo, CSE (March 2001)
Srihari, S.N., Cha, S.-H., Arora, H., Lee, S.: Individuality of Handwriting. Journal of Forensic Sciences 47(4), 856–872 (2002)
Pankanti, S., Prabhakar, S., Jain, A.K.: On the Individuality of Fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1010–1025 (2002)
Stoney, D., Thornton, J.: A Critical Analysis of Quantitative Fingerprint Individuality Models. Journal of Forensic Sciences 31(4), 1187–1216 (1986)
Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: Guide to Biomet-rics. Springer Professional Computing, (2003) ISBN 0-387-40089-3
Cha, S., Srihari, S.N.: Writer Identification: Statistical Analysis and Dichotomizer. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 123–132. Springer, Heidelberg (2000)
Kam, M., Fielding, B., Conn, R.: Writer Identification by Professional Document Ex-aminers. Jouranl of Forensic Sciences, vol 42, 778–786 (1997)
Cha, S., Srihari, S.N.: Multiple Feature Integration for Writer Verification. In: Pro-ceedings of 7th IWFHR2000, Amsterdam, Netherlands, p 333–342 (September 2000) ISBN 90-76942-01-3
Daugman, J.G.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12) (2003)
Kee, G., Byun, Y., Lee, K., Lee, Y.: Improved Techniques for an Iris Recognition System with High Performance. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2256, p. 177. Springer, Heidelberg (2001)
Mallat, S.G.: A theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Recognition and Machine Intelligence 11(4), 674–693 (1989)
Choi, S., Yoon, S., Cha, S.H., Tappert, C.C.: Use of Histogram Distances in Iris Au-thentication. In: Proceedings of International Conference on Machine Learning; Models, Technologies and Applications, Las Vegas, June 21-24 (2004)
Cha, S., Srihari, S.N.: On Measuring the Distance between Histograms. Pattern Recognition 35(6), 1355–1370 (2002)
Cha, S.: Fast Image Template and Dictionary Matching Algorithms. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1351, pp. 370–377. Springer, Heidelberg (1997)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2000)
Cherkassky, V., Friedman, J.H., Wechsler, H.: From Statistics to Neural Networks. In: Theory and Pattern Recognition Applications, NATO ASI ed., Springer, Heidelberg (1994)
Osuna, E.E., Freund, R., Girosi, F.: Support Vector Machines: Training and Applications. MIT Artificial Intelligence Laboratory and Center for Biological and Computational Learning Department of Brain and Cognitive Sciences. A.I. Memo No 1602, C.B.C.L. Paper No 144 (1997)
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Yoon, S., Choi, SS., Cha, SH., Lee, Y., Tappert, C.C. (2005). On the Individuality of the Iris Biometric. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_135
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DOI: https://doi.org/10.1007/11559573_135
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
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