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Curve Mapping Based Illumination Adjustment for Face Detection

  • Xiaoyue Jiang
  • Tuo Zhao
  • Rongchun Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

For the robust face detection, illumination is considered as one of the great challenges. Motivated with the adaptation of the human vision system, we propose the curve mapping (CM) function to adjust the illumination conditions of the images. The lighting parameter of CM function is determined by the intensity distribution of the images. Therefore the CM function can adjust the images according to their own illumination conditions adaptively. The CM method will abandon no information of the original images and bring no noises to the images. But it will enhance the details of the images and adjust the images to the proper brightness. Consequently the CM method will make the images more discriminative. Experimental results show that it can improve the performance of the face detection with the CM method as a lighting-filter.

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References

  1. 1.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 22–38 (1998)CrossRefGoogle Scholar
  2. 2.
    Schneiderman, H., Kanade, T.: A Statistical Method for 3D Object Detection Applied to Faces and Cars. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2000)Google Scholar
  3. 3.
    Li, S.Z., Zhu, L., Zhang, Z.Q., et al.: Statistical Learning of Multi-View Face Detection. In: Proc. of the 7th European Conf. on Computer Vision (2002)Google Scholar
  4. 4.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Tran. Pattern Recognition and Machine Intelligence 19(7), 721–732 (1997)CrossRefGoogle Scholar
  5. 5.
    Shashua, A., Riklin-Raviv, T.: The Quotient Images: Class-based Re-Rendering and Recognition with Varying Illuminations. IEEE Tran. Pattern Recognition and Machine Intelligence 23(2), 129–139 (2001)CrossRefGoogle Scholar
  6. 6.
    Wang, H., Li, S.Z., Wang, Y.: Generalized quotient image. In: IEEE Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  7. 7.
    Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible lighting conditions? In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 270–277 (1996)Google Scholar
  8. 8.
    Georghiads, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Tran. Pattern Recognition and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  9. 9.
    Ramamoorith, R., Hanrahan, P.: A signal-processing framework for inverse rendering. In: SIGGRAPH, pp. 117–128 (2001)Google Scholar
  10. 10.
    Basri, R., Jacobs, D.: Lambertian reflectance and linear subspace. IEEE Tran. Pattern Analysis and Machine Intelligence 25(2), 218–233 (2003)CrossRefGoogle Scholar
  11. 11.
    Ferwerda, J.A., Pattanaik, S.N., Shirley, P., Greenberg, D.P.: A model of visual adaptation for realistic image synthesis. In: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques (1998)Google Scholar
  12. 12.
    Reinhard, E., Stark, M., Shirly, P., Ferwerda, J.: Photographic tone reproduction for digital images. ACM Transactions on Graphics 21(3), 267–276 (2002)CrossRefGoogle Scholar
  13. 13.
    Xiao, R., Li, M., Zhang, H.: Robust multipose face detection in Images. IEEE trans. on Circuits and Systems for video technology 12(1), 31–41 (2004)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and expression (PIE) database. In: Processing of the IEEE International Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  15. 15.
    Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report #24 (1998)Google Scholar
  16. 16.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoyue Jiang
    • 1
  • Tuo Zhao
    • 2
  • Rongchun Zhao
    • 1
  1. 1.College of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Mechatronic EngineeringNorthwestern Polytechnical UniversityXi’anChina

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