Illumination invariant face recognition

Pattern Recognition

Volumn 38, Issue 10, 2005, Pages 1705-1716

  Liu, Dang Hui ^a,b,c   Lam, Kin Man ^a   Shen, Lan Sun ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b BEIJING UNIVERSITY OF TECHNOLOGY   (China)

^c ACADEMY OF EQUIPMENT COMMAND AND TECHNOLOGY   (China)

Author keywords

Adaptive filter; Edge extraction; Lambertian reflectance; Optimal threshold segmentation; Principal component analysis; Ratio image

Indexed keywords

ADAPTIVE FILTERING; ALGORITHMS; EDGE DETECTION; FEATURE EXTRACTION; IMAGE SEGMENTATION; LIGHTING; PRINCIPAL COMPONENT ANALYSIS;

ADAPTIVE FILTERS; EDGE EXTRACTION; LAMBERTIAN REFLECTANCE; OPTIMAL THRESHOLD SEGMENTATION; RATIO IMAGE;

FACE RECOGNITION;

EID: 22844432565     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.03.009     Document Type: Article

References (33)

1. R. Chellappa, C.L. Wilson, and S. Sirohey Human and machine recognition of faces: a survey Proc. IEEE 83 5 1995 705 740
2. W. Zhao, R. Chellappa, A. Rosenfeld and P.J. Phillips, Face recognition: A Literature Survey, CS-Tech Report-4167, University of Maryland, 2000.
3. D.H. Liu, K.M. Lam, and L.S. Shen Face recognition: a survey J. Circuits Systems 9 1 2004 85 94
4. M. Turk, and A. Pentland Eigenfaces for recognition J. Cogn. Neurosci. 3 1991 71 86
5. P.N. Belhumeur, J.P. Hespanha, and D.J. Kriengman Eigenfaces vs. fisherfaces: recognition using class specific linear projection IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 711 720
6. P.S. Penev, and J.J. Atick Local feature analysis: a general statistical theory for object representation Network Comput. Neural System 7 3 1996 477 500
7. L. Wiskott, J.M. Fellows, and N. KruK ger Face recognition by elastic bunch graph matching IEEE Trans. Pattern Anal. Mach. Intell. 19 1997 775 779
8. M.J. Er, S. Wu, and J. Lu Face recognition with radial basis function (RBF) neural networks IEEE Trans. Neural Networks 13 3 2002 697 710
9. A. Lanitis, C.J. Taylor, and T.F. Cootes Automatic face identification system using flexible appearance models Image Vision Comput. 12 1995 393 401
10. Volker Blanz, and Thomas Vetter Face recognition based on fitting a 3D morphable model IEEE Trans. Pattern Anal. Mach. Intell. 25 9 2003 1 12
11. N. Nefian, M.H. Hayes, Face recognition using an embedded HMM, in: Proceedings of the IEEE Conference on Audio and Video-based Biometric Person Authentication, 1999, pp. 19-24.
12. G. Guo, S.Z. Li, and K.L. Chan Support vector machines for face recognition Image Comput. 19 2001 631 638
13. X. Yongsheng Gao, and Maylor K.H. Leung Face recognition using line edge map IEEE Trans. Pattern Anal. Mach. Intell. 24 6 2002 764 779
14. Baofeng Guo, Kin-Man Lam, Kwan-Ho Lin, and Wan-Chi Siu Huamn face recognition based on spatially weighted Hausdorff distance Pattern Recognition Lett. 24 2003 499 507
15. Y. Adini, Y. Moses, and S. Ullman Face recognition: the problem of compensating for changes in illumination direction IEEE Trans. Pattern Anal. Mach. Intell. 19 7 2001 721 732
16. W. Zhao, R. Chellappa, SFS based view synthesis for robust face recognition, Proceedings of the 4th Conference on Automatic Face and Gesture Recognition, 2000.
17. A. Shashua, and R.R. Tammy The quotient image: class-based re-rendering and recognition with varying illuminations IEEE Trans. Pattern Anal. Mach. Intell. 23 2 2001 129 139
18. A. Shashua On Photometric Issues in 3D Visual Recognition from a Single 2D Image Int. J. Computer Vision 21 1-2 1997 99 122
19. A.U. Batur, M.H. I.I.I. Hayes, Linear subspaces for illumination robust face recognition, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2001, pp. II-296-II-301.
20. P.W. Hallinan, A low-dimensional representation of human faces for arbitrary lighting conditions, Proc. CVPR (1994) 995-999.
21. A.S. Georghiades, P.N. Belhumeur, and D.J. Kriegman From few to many: illumination cone models for face recognition under variable lighting and pose IEEE Trans. Pattern Anal. Mach. Intell. 23 2 2001 643 660
22. R. Ishiyama, S. Sakamoto, Geodesic illumination basis: compensating for illumination variations in any pose for face recognition, Proceedings of the 16th International Conference on Pattern Recognition, vol. 4 (2002) 297-301.
23. T. Shakunaga, K. Shigenari, Decomposed eigenface for face recognition under various lighting conditions, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. I-864-I-871.
24. R. Basri, and D.W. Jacobs Lambertian reflectance and linear subspaces IEEE Trans. Pattern Anal. Mach. Intell. 25 2 2003 218 233
25. K.C. Lee, J. Ho, and D. Kriegman, Nine points of light: acquiring subspaces for face recognition under variable lighting, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. I-519-I-526.
26. T. Sim, T. Kanade, Combining models and exemplars for face recognition: an illuminating example, in: Proceedings of the Workshop on Models versus Exemplars in Computer Vision, 2001.
27. W. Gao, S.G. Shan, X.J. Chai, X.W. Fu, Virtual face generation for illumination and pose intensitive face recognition, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, 2003, pp. 776-779.
28. T.F. Cootes, C.J. Taylor, D. Cooper, and J. Graham Active shape models - their training and application Computer Vision and Image Understand 61 1 1995 38 59
29. N. Otsu A threshold selection method from gray-level histograms IEEE Trans. Systems, Man, and Cybernet. 9 1 1979 62 66
30. Canny, and John A computational approach to edge detection IEEE Trans. Pattern Anal. Mach. Intell. 8 6 1986 679 698
31. Yale University, the Yale and YaleB database: http://cvc.yale.edu/projects/yalefacesB, and http://cvc.yale.edu/projects/yalefaces.
32. J. Phillips, H. Moon, and S.A. Rizvi The FERET evaluation methodology for face-recognition algorithms IEEE Trans. Pattern Anal. Mach. Intell. 22 10 2002 1090 1104
33. D.H. Liu, K.M. Lam, and L.S. Shen Optimal sampling of Gabor features for face recognition Pattern Recognition Lett. 25 2 2004 267 276