Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Pupil using Template Matching Based on Genetic Algorithm in Facial Images

얼굴 영상에서 유전자 알고리즘 기반 형판정합을 이용한 눈동자 검출

  • 이찬희 (동의대학교 디지털미디어공학과) ;
  • 장경식 (동의대학교 멀티미디어공학과)
  • Published : 2009.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a robust eye detection method using template matching based on genetic algorithm in the single facial image. The previous works for detecting pupil using genetic algorithm had a problem that the detection accuracy is influnced much by the initial population for it's random value. Therefore, their detection result is not consistent. In order to overcome this point we extract local minima in the facial image and generate initial populations using ones that have high fitness with a template. Each chromosome consists of geometrical informations for the template image. Eye position is detected by template matching. Experiment results verify that the proposed eye detection method improve the precision rate and high accuracy in the single facial image.

본 논문에서는 다양한 조명하에서의 단일 얼굴 영상에 대해 유전자 알고리즘과 형판 정합을 이용하여 빠르게 눈동자를 검출하는 방법을 제안한다. 유전 알고리즘을 이용한 기존의 눈동자 검출 방법은 초기 개체군의 위치에 민감하여 낮은 눈 검출율을 보이며, 도한 그 결과가 일관적이지 않은 문제점을 갖는다. 이와 같은 문제점을 해결화기 위해 얼굴영상에서 지역적 최소치를 추출하고 형판과 가장 높은 적합도를 가지는 개체들로 초기 개체군을 생성 하였다. 각각의 개체는 형판의 기하학적 변환 정보로 구성되며, 형판 정합에 의해 눈동자가 검출된다. 실험을 통하여 본 논문에서 제안한 눈 후보 검출을 통하여 단일 영상에서도 눈 검출의 정확도와 높은 검출률을 확인하였다.

Keywords

References

  1. Hyoung-Joon Kim, Whoi-Yul Kim, "Eye Detection in Facial Images Using Zernike Moments with SVM, [ETRI]ETRI Journal-제30권 제2호, 2008.4
  2. A.L. Yuille, P.W. Hallinan, D.S. Cohen, "Feature extraction from faces using deformable templates", Int. J. Comput. Vision 8 (2) (1992) 99–111 https://doi.org/10.1007/BF00127169
  3. A. Pentland, B. Moghaddam, and Thad Starner, "View-based and modular eigenspaces for face recongnition", In Proceedings of IEEE Conference on Computer Vision and Pattern Recongnition, pp.84-91,1994
  4. T. Kawaguchi, and M. Rizon, "Iris detection using intensity and edge information", Pattern Recognition, Vol. 36, Num. 22, pp. 549-562, 2003
  5. Fei Zuo, Peter H.N. de With. "Real-time Face Detection and Feature Localization for Consumer Applications," Proceedings of the PROGRESS/STW, pp.257-262, 2003
  6. Jürgen Rurainsky, Peter Eisert, "Template-Based Eye and Mouth Detection for 3D Video Conferencing," LNCS, Vol. 2849, pp.23-31, 2003
  7. A. Cagatay Talay, "An Approach for Eye Detection Using Parallel Genetic Algorithm" HComputational Science – ICCS 2005
  8. T. Akashi, Y. Wakasa, K. Tanaka, S. G. Karungaru, and M. Fukumi, "Genetic eye detection using artificial template," Journal of Signal Processing, vol. 10, no. 6, pp. 453–463, November 2006
  9. Nishimura, T. Nakashige, M. Akashi, T. Wakasa, Y. Tanaka, K "Eye interface for physically impaired people by Genetic Eye Tracking", SICE Annual Conference 2007 Sept. 17-20, 2007
  10. Hitoshi Iba. Genetic Algorithms. IGAKUSHUPPAN, Tokyo, Japan, 2002
  11. Kjellström, G. (January 1996). "Evolution as a statistical optimization algorithm". Evolutionary Theory (11): 105–117
  12. Nowozin, Sebastian (2005). "Hautopano-siftH". Retrieved on H2008H-H08-20H
  13. Scovanner, Paul; Ali, S; Shah, M (2007). "A 3-dimensional sift descriptor and its application to action recognition". Proceedings of the 15th International Conference on Multimedia: 357-360. HdoiH: H10.1145/1291233.1291311
  14. L. Davis, Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
  15. R. A. Caruana and J. D. Schaffer, "Representation and hidden bias: Gray vs. binary coding for genetic algorithms," in Fifth International Conference on Machine Learning, San Mateo, USA, 1988, pp. 153–161
  16. D. E. Goldberg, Genetic Algorithms in search optimization & Machine lerningn. Boston, MA, USA: Addison-Wesley Publishing Campany, Inc., 1989