Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

A Method for Finger Vein Recognition using a New Matching Algorithm

새로운 정합 알고리즘을 이용한 손가락 정맥 인식 방법

  • 김희승 (서울시립대학교 컴퓨터과학부) ;
  • 조준희 (서울시립대학교 컴퓨터과학부)
  • Received : 2010.04.19
  • Accepted : 2010.09.14
  • Published : 2010.11.15
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a new method for finger vein recognition is proposed. Researchers are recently interested in the finger vein recognition since it is a good way to avoid the forgery in finger prints recognition and the inconveniences in obtaining images of the iris for iris recognition. The vein images are processed to obtain the line shaped vein images through the local histogram equalization and a thinning process. This thinned vein images are processed for matching, using a new matching algorithm, named HS(HeeSung) matching algorithm. This algorithm yields an excellent recognition rate when it is applied to the curve-linear images processed through a thinning or an edge detection. In our experiment with the finger vein images, the recognition rate has reached up to 99.20% using this algorithm applied to 650finger vein images(130person ${\times}$ 5images each). It takes only about 60 milliseconds to match one pair of images.

이 논문에서 손가락 정맥영상에 대한 새로운 인식 방법을 제시한다. 손가락 정맥인식은 대중적으로 사용되고 있는 지문인식의 위조가능성을 배제할 수 있고, 홍채인식의 불편한 영상획득 방식을 피할 수 있는 좋은 개인 인중 방편으로 주목 받고 있다. 손가락 정맥영상을 지역적 히스토그램 균등화에 의하여 전처리하고, 이것을 세선화 처리하여 선 형태의 정맥을 얻는다. 이렇게 얻어진 선 형태의 정맥선 영상에 HS정합 알고리즘(HeeSung's Matching Algorithm) 이라고 명명된 새로운 정합 알고리즘을 적용하여 정맥의 정합 여부를 가린다. 이 새로운 정합 알고리즘은 세선화나 에지 검출 처리한 여러 가지 선 모양의 영상인식에 좋은 효과를 보이고 있다. 개인당 5편씩 총 130명분 650편의 손가락 영상에 대한 인식실험 결과 99.20%의 인식률을 보였다. 한 쌍의 영상 정합처리에 단 60ms 의 처리 속도를 보였다.

Keywords

References

  1. A. Jain, A. Ross, S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, vol.14, no.1, January 2004.
  2. X. Chen, P. Flynn, K. Bower, "Visible-light and infrared face recognition," Proceedings of the workshop on multimodal user authentication, pp. 48-55, 2003.
  3. W. Boles, B. Boashash, "A human identification technique using images of the iris and wavelet transform," IEEE Trans. on Signal Process, pp. 1185-1188, 1998.
  4. A. Kumar and D. Zhang, "Personal recognition using hand shape and texture," IEEE Trans. on Image Processing, vol.15, no.8, pp.2454-2461, 2006. https://doi.org/10.1109/TIP.2006.875214
  5. A. Kumar, K. Prathyusha, "Personal Authentication using Hand Vein Triangulation and Knuckle Shape," IEEE Transactions on Image Processing, 2009.
  6. Y. Ding, D. Zhuang, K. Wang, "A study of hand vein recognition method," IEEE International Conference on Mechatronics and Automation, vol.4, pp.2106-2110, 2005.
  7. S. Im, H. Park, Y. Kim, S. Han, S. Kim, C. Kang, C. Chung, "A Biometric identification system by extracting hand vein patterns," J. Korean Phys. Soc., vol.28, no.3, pp.268-272, 2001.
  8. Fujitsu-Laboratories-Ltd. Fujitsu Laboratories Develops Technology For World's First Contactless Palm Vein Pattern Biometric Authentication System. March 31, 2003.
  9. L. Wang and G. Leedham, "A thermal hand vein pattern verification system," ICAPR, LNCS 3687, pp.58-65, 2005.
  10. N. Miura et al., "Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification," Machine Vision and Applications, vol.25, pp.194-203, 2004.
  11. S. Im et al., "A Direction-Based Vascular Pattern Extraction Algorithm for Hand Vascular Pattern Verification," ETRI J., vol.25, no.2, pp.101-108, Apr. 2003. https://doi.org/10.4218/etrij.03.0102.0211
  12. X. Li et. al., "Vein Pattern Recognitions by Moment invariants," First International Conference on Bioinformatics and Biomedical Engineering, pp.612-615, Jul, 2007.
  13. L. Zhang, R. Zhang, C. Yu, "Study on the Identity Authentication System for Finger Vein," The 2nd International Conference on Bioinformatics and Biomedical Engineering, pp.1905-1907, May 2008.
  14. Y. Ding, D. Zhuang, K. Wang, "A study of hand vein recognition method," IEEE International Conference on Mechatronics and Automation, vol.4, pp.2106-2110, Aug., 2005.
  15. G. Venayagamoorthy, V. Moonasar, K. Sandrasegaran, "Voice recognition using neural networks," Proceedings of the IEEE South African Symposium on Communication and Signal Processing, pp.29-32, 1988.
  16. S. Sarkar, P. Phillips, Z. Liu, I. Vega, P. Grother, and K. Bowyer, "The Human ID gait challenge problem: data sets, performance, and analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.2, pp.162-177, Feb. 2005. https://doi.org/10.1109/TPAMI.2005.39
  17. A. Hoover, V. Kouznetsova, M. Goldbaum, "Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response," IEEE Trans. on Med. Imag., pp.203-210, 2000.
  18. T. Walter, J. Klein, P. Massin, F. Zana, "Automatic segmentation and registration of retinal fluorescein angiographies - application to diabetic retinopathy," Proceedings of the 1st International Workshop on Computer Assisted Image Analysis, pp.15-20, May 2000.
  19. P. Montesinos, L. Alquier, (1996) Perceptual organization of thin networks with active contour functions applied to medical and aerial images, Proceedings of ICPR'96, pp.25-30, 1996.
  20. T. Zhang, C. Suen. "A fast parallel algorithm for thinning digital patterns," Communications of the ACM, vol.27, no.3, pp.236-239, 1984. https://doi.org/10.1145/357994.358023
  21. 특허출원 중, 1-1-2009-0495547-41