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Illumination-Robust Face Recognition based on Illumination-Separated Eigenfaces

조명분리 고유얼굴에 기반한 조명에 강인한 얼굴 인식

  • 설태인 (숭실대학교 정보통신 전자공학부) ;
  • 정선태 (숭실대학교 정보통신 전자공학부) ;
  • 조성원 (홍익대학교 지능정보처리연구실)
  • Published : 2009.02.28

Abstract

The popular eigenfaces-based face recognition among proposed face recognition methods utilizes the eigenfaces obtained from applying PCA to a training face image set. Thus, it may not achieve a reliable performance under illumination environments different from that of training face images. In this paper, we propose an illumination-separate eigenfaces-based face recognition method, which excludes the effects of illumination as much as possible. The proposed method utilizes the illumination-separate eigenfaces which is obtained by orthogonal decomposition of the eigenface space of face model image set with respect to the constructed face illumination subspace. Through experiments, it is shown that the proposed face recognition method based on the illumination-separate eigenfaces performs more robustly under various illumination environments than the conventional eigenfaces-based face recognition method.

Keywords

Face Recognition;Illumination;Eigenfaces;PCA;Biometrics

References

  1. S. Z. Li and A. K. Jain, Handbook of Face Recognition, 2004.
  2. M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve procedure for the characterization of human faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, pp.103-108, 1990. https://doi.org/10.1109/34.41390
  3. A. Pentland and M. Turk, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, Vol.3, pp.71-86, 1993. https://doi.org/10.1162/jocn.1991.3.1.71
  4. A. Lemieux and M. Parizeau, "Experiments on Eigenfaces Robustness," Proc. International Conf. on Pattern Recognition (ICPR) 2002.
  5. E. H. Land and J. J. McCann, "Lightness and retinex theory," Journal of the Optical Society of America, pp.61:1-11, 1971. https://doi.org/10.1364/JOSA.61.000001
  6. I. T. Jollie, Principal Component Analysis, Springer - Verlag, New York, 1986.
  7. R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination invariant face recognition," In Audio-and Video-Based Biometric Person Authentication, Vol.2688, pp.10-18, 2003(6). https://doi.org/10.1007/3-540-44887-X_2
  8. 김상훈, 정선태, 정수환, 조성원, "얼굴 인식을 위한 Anisotropic Smoothing 기반 효율적 조명 전처리", 한국콘텐츠학회논문지, pp.236-245, 2008(1) https://doi.org/10.5392/JKCA.2008.8.1.236
  9. http://cvc.yale.edu/projects/yalefaces/yalefaces.html
  10. B. Horn, Robot Vision, MIT Press, 1986.
  11. T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Anal. Mach. Intell., Vol.23, No.6, pp.681-685, 2001(6). https://doi.org/10.1109/34.927467
  12. F. Kahraman, M. Gokmen, S. Darkner and R. Larsen, "An Active Illumination and Appearance (AIA) Model for Face Alignment," Proc. of the CVPR 2007, IEEE Computer Society Workshop on Biometrics, 2007. https://doi.org/10.1109/CVPR.2007.383399
  13. 김상훈, 정선태, 정수환, 전승선, 김재민, 조성원"AAM과 가버 특징 벡터를 이용한 강인한 얼굴인식 시스템", 한국콘텐츠학회 제7권, 제2호, pp.1-10, 2007(2). https://doi.org/10.5392/JKCA.2007.7.2.001