Abstract
We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination direction lie in a 3-D linear subspace of the high dimensional feature space — if the face is a Lambertian surface without self-shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant and produces well separated classes in a low-dimensional subspace even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
J. Hespanha was supported by NSF Grant ECS-9206021, AFOSR Grant F49620-94-1-0181, and ARO Grant DAAH04-95-1-0114.
D. Kriegman was supported by NSF under an NYI, IRI-9257990 and by ONR N00014-93-1-0305
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References
P. Belhumeur and D. Kriegman. What is the set of images of an object under all possible lighting conditions? In IEEE Proc. Conf. Computer Vision and Pattern Recognition, 1996.
D. Beymer. Face recognition under varying pose. In Proc. Conf. Computer Vision and Pattern Recognition, pages 756–761, 1994.
R. Brunelli and T. Poggio. Face recognition: Features vs templates. IEEE Trans. Pattern Anal. Mach. Intelligence, 15(10):1042–1053, 1993.
R. Chellappa, C. Wilson, and S. Sirohey. Human and machine recognition of faces: A survey. Proceedings of the IEEE, 83(5):705–740, 1995.
Q. Chen, H. Wu, and M. Yachida. Face detection by fuzzy pattern matching. In Int. Conf. on Computer Vision, pages 591–596, 1995.
Y. Cheng, K. Liu, J. Yang, Y. Zhuang, and N. Gu. Human face recognition method based on the statistical model of small sample size. In SPIE Proc: Intelligent Robots and Computer Vision X: Algorithms and Techn., pages 85–95, 1991.
I. Craw, D. Tock, and A. Bennet. Finding face features. In Proc. European Conf. on Computer Vision, pages 92–96, 1992.
Y. Cui, D. Swets, and J. Weng. Learning-based hand sign recognition using SHOSLIF-M. In Int. Conf. on Computer Vision, pages 631–636, 1995.
R. Duda and P. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1973.
R. Fisher. The use of multiple measures in taxonomic problems. Ann. Eugenics, 7:179–188, 1936.
A. Gee and R. Cipolla. Determining the gaze of faces in images. Image and Vision Computing, 12:639–648, 1994.
J. Gilbert and W. Yang. A Real-Time Face Recognition System Using Custom VLSI Hardware. In Proceedings of IEEE Workshop on Computer Architectures for Machine Perception, pages 58–66, 1993.
P. Hallinan. A low-dimensional representation of human faces for arbitrary lighting conditions. In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pages 995–999, 1994.
P. Hallinan. A Deformable Model for Face Recognition Under Arbitrary Lighting Conditions. PhD thesis, Harvard University, 1995.
J. Hespanha, P. Belhumeur, and D. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. Center for Systems Science 9506, Yale University, PO Box 208267, New Haven, CT 06520, May 1995.
B. Horn. Computer Vision. MIT Press, Cambridge, Mass., 1986.
A. Lanitis, C. Taylor, and T. Cootes. A unified approach to coding and interpreting face images. In Int. Conf. on Computer Vision, pages 368–373, 1995.
T. Leung, M. Burl, and P. Perona. Finding faces in cluttered scenes using labeled random graph matching. In Int. Conf. on Computer Vision, pages 637–644, 1995.
K. Matsuno, C. Lee, S. Kimura, and S. Tsuji. Automatic recognition of human facial expressions. In Int. Conf. on Computer Vision, pages 352–359, 1995.
Moghaddam and Pentland. Probabilistic visual learning for object detection. In Int. Conf. on Computer Vision, pages 786–793, 1995.
Y. Moses, Y. Adini, and S. Ullman. Face recognition: The problem of compensating for changes in illumination direction. In European Conf. on Computer Vision, pages 286–296, 1994.
H. Murase and S. Nayar. Visual learning and recognition of 3-D objects from appearence. Int. J. Computer Vision, 14(5–24), 1995.
A. Pentland, B. Moghaddam, and Starner. View-based and modular eigenspaces for face recognition. In Proc. Conf. Computer Vision and Pattern Recognition, pages 84–91, 1994.
A. Samal and P. Iyengar. Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition, 25:65–77, 1992.
A. Shashua. Geometry and Photometry in 3D Visual Recognition. PhD thesis, MIT, 1992.
W. Silver. Determining Shape and Reflectance Using Multiple Images. PhD thesis, MIT, Cambridge, MA, 1980.
Sirovitch, L. and Kirby, M., Low-dimensional procedure for the characterization of human faces. J. Optical Soc. of America A, 2:519–524, 1987.
M. Turk and A. Pentland. Eigenfaces for recognition. J. of Cognitive Neuroscience, 3(1), 1991.
M. Turk and A. Pentland. Face recognition using eigenfaces. In Proc. IEEE Conf. on Comp. Vision and Patt. Recog., pages 586–591, 1991.
R. Woodham. Analysing images of curved surfaces. Artificial Intelligence, 17:117–140, 1981.
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Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J. (1996). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015522
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