Browse > Article
http://dx.doi.org/10.6109/JKIICE.2009.13.7.1429

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

Lee, Chan-Hee (동의대학교 디지털미디어공학과)
Jang, Kyung-Shik (동의대학교 멀티미디어공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.13, no.7, 2009 , pp. 1429-1436 More about this Journal
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.
Keywords
얼굴인식;눈동자 검출;유전자 알고리즘;형판 정합;
Citations & Related Records
연도 인용수 순위
  • Reference
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   DOI
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. 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
5 Kjellström, G. (January 1996). "Evolution as a statistical optimization algorithm". Evolutionary Theory (11): 105–117
6 D. E. Goldberg, Genetic Algorithms in search optimization & Machine lerningn. Boston, MA, USA: Addison-Wesley Publishing Campany, Inc., 1989
7 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
8 Nowozin, Sebastian (2005). "Hautopano-siftH". Retrieved on H2008H-H08-20H
9 A. Cagatay Talay, "An Approach for Eye Detection Using Parallel Genetic Algorithm" HComputational Science – ICCS 2005
10 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
11 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
12 Jürgen Rurainsky, Peter Eisert, "Template-Based Eye and Mouth Detection for 3D Video Conferencing," LNCS, Vol. 2849, pp.23-31, 2003
13 L. Davis, Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
14 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
15 T. Kawaguchi, and M. Rizon, "Iris detection using intensity and edge information", Pattern Recognition, Vol. 36, Num. 22, pp. 549-562, 2003   ScienceOn
16 Hitoshi Iba. Genetic Algorithms. IGAKUSHUPPAN, Tokyo, Japan, 2002