Abstract
Recognizing human face is one of the most important part in biometrics. However, drastic change of facial pose makes it a difficult problem. In this paper, we propose linear pose transformation method in feature space. At first, we extracted features from input face image at each pose. Then, we used extracted features to transform an input pose image into its corresponding frontal pose image. The experimental results show that recognition rate with pose transformation is much better than the result without pose transformation.
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References
Zhang, D.: AUTOMATED BIOMETRICS-Technologies and Systems. kluwer academic publishers, Dordrecht (2000)
Beymer, D.: Face recognition under varying pose. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 756–761 (1994)
Biuk, Z., Loncaric, S.: Face recognition from multi-pose image sequence. In: Proceedings of 2nd Int’l Symposium on Image and Signal Processing and Analysis (2001)
Huang, F., Zhou, Z., Zhang, H., Chen, T.: Pose invariant face recognition. In: Proceedings of the 4th 1EEE International Conference on Automatic Face and Gesture Recognition, pp. 245–250 (2000)
Eremad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. Journal of Optical Society of America 14, 1724–1733 (1997)
Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 22, 2385–2404 (2000)
Bressan, M., Vitria, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24, 2743–2749 (2003)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Boston (1990)
Hotelling, H.: Analysis of a compltex statistical variables into principal components. Journal of Educational Psychology 24, 417–441 (1933)
Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)
Belhumeur, P., Hespanha, J., Kriegman, D.: 1997. eigenfaces vs. fisherfaces: Class specific linear projection. PAMI 19, 711–720 (1997)
Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. Journal of Optical Society of America A 14, 1724–1733 (1997)
Duc, B., Fischer, S., Bigun, J.: Face authentication with gabor information on deformable graphs. IEEE Transactions on Image Processing 8, 504–516 (1999)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2001)
Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)
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© 2004 Springer-Verlag Berlin Heidelberg
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Lee, HS., Kim, D. (2004). Pose Invariant Face Recognition Using Linear Pose Transformation in Feature Space. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. CVHCI 2004. Lecture Notes in Computer Science, vol 3058. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24837-8_20
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DOI: https://doi.org/10.1007/978-3-540-24837-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22012-1
Online ISBN: 978-3-540-24837-8
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