Eye Localization based on Multi-Scale Gabor Feature Vector Model

다중 스케일 가버 특징 벡터 모델 기반 눈좌표 검출

  • Published : 2007.01.28


Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications. In this paper, we propose an improved eye localization method based on multi-scale Gator feature vector models. The proposed method first tries to locate eyes in the downscaled face image by utilizing Gabor Jet similarity between Gabor feature vector at an initial eye coordinates and the eye model bunch of the corresponding scale. The proposed method finally locates eyes in the original input face image after it processes in the same way recursively in each scaled face image by using the eye coordinates localized in the downscaled image as initial eye coordinates. Experiments verify that our proposed method improves the precision rate without causing much computational overhead compared with other eye localization methods reported in the previous researches.


Eye Localization;Gabor Features Vectors;Eye Model Bunch;Multi-Scale Approach;Gabor Wavelets


  1. S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004
  2. P. Wang, M. B. Green, Q. Ji, and J. Wayman, "Automatic Eye Detection and Its Validation," Computer Vision and Pattem Recognition 2005 IEEE Computer Society Conference, Vol.3, pp.164-172, June 2005.
  3. T. Kawanguchi,D. Hidaka, and M Rizon, "Robust Extraction of Eyes From Face," 15th lnt'/ Conf. on Pattern Recognition, Vol.1, pp.1109-1114, Sept. 2000
  4. O. Jesorsky, K. Kirchberg,and R Frischholz,"Robust Face Detection Using the Hausdorff Distance," In: J. Bigun, F. Smeraldi Eds. Lecture Notes in computer Science 2091, Berlin: Springer, pp.90-95, 2001.
  5. H. Zhou and X. Geng, ''Projection Functions for Eye Detection," Pattern Recognition, No.5, pp.1049-1056,May 2004.
  6. Y. Ma, X. Ding, Z. Wang, and N. Wang, ''Robust Precise Eye Location under Probabilistic Framework," Proc. 6th lEEE lnt' Conf. on Automatic fce and Gesture Recognition (FGR'04), pp.339-344, May 2004.
  7. P. Campadelli, R Lanzarotti, and G. Lipori, ''Precise eye locaIization through a general-to-specific model definition," Proc. 17th conference organised by the British Machine Vision (BMVC2006), 2006.
  8. Z. Niu, S. Shan,S. Yan, X. Chen, and W. Gao, "2D Cascaded Adaboost for Eye Localization," 18th lnt'l Conf. on Pattern Recognition, Vol.2, pp.1216-1219, Aug. 2006.
  9. R. Lienhart and J. Maydt, "An Extended Set of Haar-like Features for Rapid Object Detection," lEEE ICCP 2002, Vol.1, pp.900-903, Sept. 2002
  10. L. Wiskott, J. M Fellous, N. Kuiger, and C. vonder Malsburg, "Face Recognition by Elastic Bunch Graph Matching," Pattern Analysis and Machine Intelligence, lEEE Transactions, Vol.19, pp.775-779, July 1997.
  11. D. V. Bolme, Elastic Bunch Matching, Master's Thesis, Colorado State University, 2003.
  12. J. K. Kamarainen and V. Kyrki, "Invariance Properties of Gabor Filter-Based Features Overview and Applicaions," lEEE Trans. Omage Processing, Vol.15, No.5, pp.1088-1099,May 2006.
  13. 정진권, 자세와 표정변화에 강인한 눈 위치 검출에 관한 연구, 홍익대학교 대학원석샤논문,2006(2)