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An Illumination-Insensitive Face Matching Algorithm

  • Chyuan-Huei Thomas Yang 
  • Shang-Hong Lai 
  • Long-Wen Chang 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2532)

Abstract

Face matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, an illumination-insensitive face imagematching algorithm is proposed. This algorithm is based on an accumulated consistency measure of corresponding normalized gradients at face contour locations between two comparing face images under different lighting conditions. To solve the matching problem due to lighting changes between two face images, we first use a consistency measure, which is defined by the inner product between two normalized gradient vectors at the corresponding locations in the two images. Then we compute the sum of the individual consistency measures of the normalized gradients at all the contour pixels to be the robust matching measure between two face images. To better compensate for lighting variations, three face images with very different lighting directions for each person are used for robust face image matching. The Yale Face Database, which contains images acquired under three different lighting conditions for each person, are used to test the proposed algorithm. The experimental results show good recognition results under different lighting conditions by using the proposed illuminationinsensitive face matching algorithm.

Keywords

Face Recognition Face Image Gesture Recognition Consistency Measure Face Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Adini, Y., Moses, Y., Ullman, S.,: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Analysis Mach. Intel., Vol. 19, No. 7 (1997) 721–732CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P. N., Hespanha, J. P., Kriegman, D. J.,: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Analysis Mach. Intel., Vol. 19, No. 7 (1997) 711–720CrossRefGoogle Scholar
  3. 3.
    Belongie, S., Malik, J., Puzicha, J.: Matching shapes. Proc. Int. Conf. Computer Vision, (2001) 454–461Google Scholar
  4. 4.
    Beymer, D., Poggio, T.: Face recognition from one example view. MIT AI Memo No. 1536 (1995)Google Scholar
  5. 5.
    Edwards, G. J., Taylor, C. J., Cootes, T. F.: Interpreting face images using active appearance models. Proc. Third IEEE Conf. on Automatic Face and Gesture Recognition (1998) 300–305Google Scholar
  6. 6.
    Georghiades, A. S., Kriegman, D. J., Belhumeur, P. N.: Illumination Cones for Recognition under Variable Lighting Faces. Proc. IEEE Conf. CVPR (1998) 52–59Google Scholar
  7. 7.
    Georghiades, A. S., Kriegman, D. J., Belhumeur, P. N.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. Pattern Analysis Mach. Intel., Vol. 23, No. 6 (2001) 643–660CrossRefGoogle Scholar
  8. 8.
    Gros, P.: Color illumination models for image matching and indexing. Proc. Int. Conf. Pattern Recognition, Vol. 3 (2000)576–579Google Scholar
  9. 9.
    Hotta, K., Mishima, T., Kurita, T., Umeyama, S.: Face matching through information theoretical attention points and its applications to face detection and classification. Proc. Fourth IEEE Conf. on Automatic Face and Gesture Recognition (2000) 34–39Google Scholar
  10. 10.
    Mojsilovic, A., Hu, J.: Extraction of perceptually important colors and similarity measurement for image matching. Proc. Int. Conf. Image Processing (2000) 61–64Google Scholar
  11. 11.
    Mu, X., Artiklar, M., Hassoun, M. H., Watta, P.: Training algorithms for robust face recognition using a template-matching approach. Proc. Int. Joint Conf. Neural Networks (2001) 2877–2882Google Scholar
  12. 12.
    Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P.: Numerical Recipes in C, 2nd Ediition, Cambridge University Press (1992)Google Scholar
  13. 13.
    Sengupta, K., Ohya, J.: An affine coordinate based algorithm for reprojecting the human face for identification tasks. Proc. International Conference on Image Processing, Vol. 3 (1997) 340–343CrossRefGoogle Scholar
  14. 14.
    Takacs, B., Wechsler, H: Face recognition using binary image metrics. Proc. Third IEEE Conf. Automatic Face and Gesture Recognition (1998) 294–299Google Scholar
  15. 15.
    Wiskott, L., Fellous, J.-M., Kuiger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI, Vol. 19, No. 7, (1997) 775–779Google Scholar
  16. 16.
    Yang, Chyuan-Huei T., Lai, Shang-Hong, Chang, Long-Wen: Robust Face Matching Under Lighting Conditions. Proc. IEEE International Conference on Multimedia and Expo, Session ThuAmPO1 No. 317 (2002)Google Scholar
  17. 17.
    Zhao, W.-Y., Chellappa, R.: Illumination-Insensitive Face Recognition using Symmetric Shape-from-Shading. Proc. IEEE Conf. CVPR (2000) 286–293Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chyuan-Huei Thomas Yang 
    • 1
  • Shang-Hong Lai 
    • 1
  • Long-Wen Chang 
    • 1
  1. 1.Department of Computer ScienceNational Tsing-Hua UniversityHsingChuR.O.C

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