Robust Face Recognition based on Gabor Feature Vector illumination PCA Model

가버 특징 벡터 조명 PCA 모델 기반 강인한 얼굴 인식

  • 설태인 (숭실대학교 대학원 전자과) ;
  • 김상훈 (숭실대학교 대학원) ;
  • 정선태 (숭실대학교 정보통신전자공학부) ;
  • 조성원 (홍익대 전자전기공학부)
  • Published : 2008.11.25


Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.


  1. S. Z. Li and A. K. Jain, 'Handbook of Face Recognition,' 2004
  2. E. H. Land and J. J. McCann, 'Lightness and retinex theory,' Journal of the Optical Society of America, pp.61:1-11, 1971
  3. 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, June 2003
  4. B. Horn, 'Robot Vision,' MIT Press, 1986
  5. J. Zou, Q. Ji and G. Nagy, 'A Comparative Study of Local Matching Approach for Face Recognition,' IEEE Trans. Image Processing, vol.16, Issue 10, pp.2617-2628, Oct 2007
  6. Yale Face database,
  7. P. Belhumeur and D. Kriegman, 'What is the set of images of an object under all possible lighting conditions?,' International Journal of Computer Vision, 28(3), pp.245-260, 1998
  8. R. Basri and D.W. Jacobs, 'Lambertian reflectances and linear subspaces,' IEEE Int. Conf. on Computer Vision, II pp. 383-390, 2001
  9. A. Georghiades, P. Belhumeur and D. Kriegman, 'From few to many: generative models for recognition under variable pose and illumination,' IEEE Trans, on Pattern Analysis and Machine Intelligence, 23(6), pp.643-660, 2001
  10. F. Kahraman and et al., 'An Active Illumination and Appearance (AIA) Model for Face Alignment,' Proc. of the CVPR 2007, IEEE Computer Society Workshop on Biometrics, 2007
  11. Y. Adini, Y. Moses and S. Ullman, 'Face Recognition: The problem of compensating for changes in illumination direction,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.721-732, 1997(7)
  12. H. Chen, P. Belhumeur and D. Jacobs, 'In search of illumination invariants,' in Proc. of IEEE Conf. Computer Vision and Pattern Recognition, pp.1-8, 2000
  13. I. T. Jollie, 'Principal Component Analysis,' Springer-Verlag, New York, 1986
  14. M. Turk and A. Pentland. 'Face recognition using eigenfaces,' In Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 586-590, Maui, Hawaii, Dec 1991
  15. BELHUMEUR, P. N., HESPANHA, J. P. AND KRIEGMAN, D. J. 'Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection,' IEEE Trans. Patt. Anal. Mach. Intell. 19, pp.711-720, 1997
  16. L. Wiskott, J. M. Fellous, N. Kuiger and C. von der Malsburg, 'Face Recognition by Elastic Bunch Graph Matching,' Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.19, pp.775-779, 1997(7)