Abstract
In this paper, we address the problem of face recognition under drastic changes of the imaging processes through which the facial images are acquired. A new method is proposed. Unlike the conventional algorithms that use only the face features, the present method exploits the statistical information of the variations between the face image sets being compared, in addition to the features of the faces themselves. To incorporate the face and perturbation features for recognition, a technique called weak orthogonalization of the two subspaces has been developed that transforms the two overlapped subspaces such that the volume of the intersection of the resulting two subspaces is minimized. Matching is performed in the transformed face space that has thus been weakly orthogonalized against perturbation space. Results using real pictures of the frontal faces from drivers' licenses demonstrate the effectiveness of the new algorithm.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
T. Kurita, N. Ohtsu and T. Sato, “A Face Recognition Method Using Higher Order Local Autocorrelation and Multivariate Analysis”, In Proc. IEEE ICPR92, pp. 213–216, 1992.
S. Akamatsu et. al., “An Accurate and Robust Face Identification Scheme”, In Proc. IEEE ICPR92, pp. 217–220, 1992.
M. Kirby, L. Sirovich, “Application of the Karhunen-Loeve procedure for the characterization of human faces”, IEEE Trans. Patt. Anal. Machine Intell., vol. 12, pp. 103–108, 1990.
K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press 1972.
M. Turk, A. Pentland, “Face recognition using eigenfaces”, In Proc. IEEE CVPR91, 1991.
M. Turk, A. Pentland, “Eigenfaces for recognition”, Journal of Cognitive Neuroscience, vol. 3, No. 1, 1991.
A. Pentland, B. Moghaddam, T. Starner, “View-based and modular eigenspaces for face recognition”, In Proc. IEEE CVPR94, 1994.
R. Brunelli, T. Poggio, “Face Recognition: Features versus Template”, IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-8, pp.34–43, 1993.
N. Belhumeur, P. Hespanha, J. Kriegman, “Eigenfaces vs. fisherfaces:recognition using class specific linear projection”, In Proc. ECCV'96 voll pp. 45–58, 1996.
K. Fukunaga, W. C. G. Koontz, ”Application of the Karhunen-Loeve expansion to feature extraction and ordering”, IEEE Trans. Computers, Vol. C-19, pp. 311–318, 1970.
K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press 1972.
D. H. Foley, J. W. Sammon, ”An Optimal Set of Discriminant Vectors”, IEEE Trans. Computers, Vol. C-24, NO. 3, pp. 281–289, March 1975.
E. Oja, J. Karhunen, ”An Analysis of Convergence for a Learning Version of the Subspace Method”, J. Math. Anal. Applications 91. pp. 102–111, 1983.
E. Oja, Subspace Methods of Pattern Recognition, Research Studies Press LTD and John Wiley & Sons Inc., 1983.
J. Kittler, ”The subspace approach to pattern recognition”, in Progress in cybernetics and systems research, p. 92, Hamisphere Publ. Co., Washington, 1978.
T. Kohonen, P. Lehtio, E. Oja, ”Spectral classification of phonemes by learning subspacees”, Helsinki University of Technology, Dept. of Technical Physics, Report TKK-F-A348. Also in Proc. IEEE ICASP, pp. 2–4, April 1979.
S. Watanabe, N. Pakvasa, ”Subspace method of pattern recognition”, In Proc. IJCPR pp. 25–32, 1973.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nagao, K., Sohma, M. (1998). Recognizing faces by weakly orthogonalizing against perturbations. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV’98. ECCV 1998. Lecture Notes in Computer Science, vol 1407. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054768
Download citation
DOI: https://doi.org/10.1007/BFb0054768
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64613-6
Online ISBN: 978-3-540-69235-5
eBook Packages: Springer Book Archive