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Efficient Iris Recognition through Improvement of Feature Vector and Classifier

Shinyoung Lim , Kwanyong Lee , Okhwan Byeon , and Taiyun Kim

Abstract :

In this paper, we propose an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to makin

Key word :

Iris recognition, feature extraction, winner selection algorithm

DOI :

http://dx.doi.org/10.4218/etrij.01.0101.0203

Cite this :

Shinyoung Lim, Kwanyong Lee, Okhwan Byeon, and Taiyun Kim, "Efficient Iris Recognition through Improvement of Feature Vector and Classifier," ETRI Journal, vol. 23, no. 2, June 2001, pp. 61-70.
http://dx.doi.org/10.4218/etrij.01.0101.0203

References :

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4. John G. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(11), 1993, pp. 1148-1161.
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7. Wildes, R.P., Asmuth, J.C. et al., "A System for Automated Iris Recognition", Proc. of the Second IEEE Workshop on Applications of Computer Vision, 1994, pp.121-128.
8. Boles, W.W. and Boashash, B., "A Human Identification Technique Using Images of the Iris and Wavelet Transform", IEEETrans. on Signal Processing, 46(4), 1998, pp.1185-1188.
9. Randy K. Young, Wavelet Theory and Its Application, Kluwer Academic Publisher, 1992.
10. Rioul O. and Vetterli M., "Wavelet and Signal Processing", IEEE Signal Processing Magazine, October 1981, pp. 14-38.
11. Gilbert Strang, Truong Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996.
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14. Ilgu Yun et al., "Extraction of Passive Device Model Parameters Using Genetic Algorithms", ETRI J., Vol. 22, No.1, 2000, pp.38-46.
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