Abstract
In this work, a new method for iris recognition based on support vector machines was proposed. The recognition consisted of three major components: image preprocessing, feature extraction and classification. Location and normalization methods were employed in image preprocessing. In iris classification and verification, an efficient approach called support vector machines was used. Experimental results show that the proposed method has an emerging performance.
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References
Daugman, J.: High Confidence Visual Recognition of Persons by Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1995)
Wildes, R., et al.: A System For Automated Iris Recognition. In: Proc. 2nd IEEEWorkshop Applicant. Computer Vision, pp. 121–128 (1994)
Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transactions on Signal Processing 46(4), 1185–1188 (1998)
Sanchez-Reillo, R., Sanchez-Avila, C.: Processing of the Human Iris Pattern for Biometric Identification. In: Proc. Of IPMU 2000. 8th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems, Madrid, Spain, pp. 653–656 (2000)
De Martin-Roche, D., Sanchez-Avila, C., Sanchez-Reillo, R.: Iris Recognition For Biometric Identification Using Dyadic Wavelet Transform Zero Crossing. In: 2001 International Carnahan Conference on Security Technology, London, England, pp. 229–234 (2001)
Ramponi, G.: Warped Distance For Space Variant Linear Image Interpolation. IEEE Trans Image Proc. 8(5), 629–639 (1999)
Li, S.T., James, T.K., Zhu, H.L., Wang, Y.N.: Texture Classification Using the Support Vector Machines. Pattern recognition 36, 2883–2893 (2003)
Burges, C.J.C.: A Tutorial On Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Joachims, T.: Making Large-Scale SVM Learning Practical. ch. 11. MIT Press, Cambridge (1998)
Jonsson, K., Kittler, J., Li, Y.P., Matas, J.: Support Vector Machines For Face Authentication. In: Pridmore, T., Elliman, D. (eds.) British Machine Vision Conference, pp. 543–553. BMVA Press (1999)
Lehmann, T.M., Gonner, C., Spitzer, K.: Survey: Interpolation Methods In Medical Image Processing. IEEE Trans Med Image 18(11), 1049–1075 (1999)
Jain, A.K., Bolle, R., Pankanti, S., et al.: Biometrics: Personal Identification In Networked Society. Kluwer Academic Publishers, Dordrecht (1999)
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Wang, Y., Han, J. (2004). Iris Recognition Using Support Vector Machines. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_102
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DOI: https://doi.org/10.1007/978-3-540-28647-9_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
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