웨이브렛 변환과 LVQ를 이용한 홍채인식 시스템

Human Iris Recognition System using Wavelet Transform and LVQ

  • 발행 : 2000.07.01

초록

The popular methods to check the identity of individuals include passwords and ID cards. These conventional method for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way.

키워드

참고문헌

  1. F. H. Adler, Physiology of the EYE: Clinical Application, The C.V. Mosby Company, 1965
  2. P. W. Hallinan, 'Recognizing Human Eyes', SPIE Proc. of Geometric Methods in Computer Vision, Vol. 1570, pp. 214-226, 1991 https://doi.org/10.1117/12.48426
  3. John G. Dougman, 'High Confidence Visual Recognition of Persons by a Test of Statistical Independence', IEEE Transaction of Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148-1161, 1993 https://doi.org/10.1109/34.244676
  4. Gerold O. Williams, 'Iris Recognition Technology', IEEE AES Systems Magazine 1997, pp. 23-29, April 1997 https://doi.org/10.1109/62.575997
  5. W.W. Boles and B. Boashash, 'A Human Identification Technique Using images of the Iris and Wavelet Transform', IEEE Transaction on Signal Processing, Vol. 46, No. 4, pp. 1185-1188, 1998 https://doi.org/10.1109/78.668573
  6. Richard P. Wildes, 'Iris Recognition: An Emerging Biometric Technology', Proceedings of the IEEE, Vol. 85, No. 9, pp. 1348-1363, 1997 https://doi.org/10.1109/5.628669
  7. http://www.iriscan.com/html/irisrecogproduct.html
  8. 조성원, 성혁인, 'Gabor 변환과 신경회로망을 이용한 홍채인식', 한국 퍼지 및 지능시스템 학회, Vol. 7, No. 2, pp. 397-401, 1997
  9. Randy K. Young, Wavelet Theory and Its Application, Kluwer Academic Publisher, 1992
  10. Michael Misiti et al., Matlab Wavelet Toolbox User's Guide, The MathWorks Inc., 1996
  11. O. Rioul and M. Vetterli, Wavelet and Signal Processing, IEEE SP Magazine, pp. 14-38, 1981 https://doi.org/10.1109/79.91217
  12. Gilbert Strang, Truong Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996
  13. L. Fausset, Fundamentals of Neural Networks, Prentice Hall, 1994
  14. T. Kohonen, The Self-organization and Associate Memory, Springer-Verlag, 1985