Abstract
In this paper, we propose a novel method named the Multiple Constrained Mutual Subspace Method which increases the accuracy of face recognition by introducing a framework provided by ensemble learning. In our method we represent the set of patterns as a low-dimensional subspace, and calculate the similarity between an input subspace and a reference subspace, representing learnt identity. To extract effective features for identification both subspaces are projected onto multiple constraint subspaces. For generating constraint subspaces we apply ensemble learning algorithms, i.e. Bagging and Boosting. Through experimental results we show the effectiveness of our method.
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References
Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proceedings IEEE Third International Conference on Automatic Face and Gesture Recognition, pp. 318–323 (1998)
Shakhnarovich, G., Fisher III, J.W., Darrell, T.: Face recognition from long-term observations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 851–865. Springer, Heidelberg (2002)
Wolf, L., Shashua, A.: Learning over Sets using Kernel Principal Angles. Journal of Machine Learning Research 4(913-931), 851–868 (2003)
Arandjelovic, O., Cipolla, R.: Face Recognition from Image Sets using Robust Kernel Resistor-Average Distance. In: The First IEEE Workshop on Face Processing in Video (2004)
Fukui, K., Yamaguchi, O.: Face Recognition Using Multi-viewpoint Patterns for Robot Vision. In: 11th International Symposium of Robotics Research, pp. 192–201 (2003)
Sato, T., Sukegawa, H., Yokoi, K., Dobashi, H., Ogata, J., Okazaki, A.: “FacePass” – Development of a Face-Recognition Security System Unaffected by Entrant’s Stance. The Institute of Image Information and Television Engineers Transactions 56(7), 1111–1117 (2002) (in Japanese)
Kozakaya, T., Nakai, H.: Development of a Face Recognition System on an Image Processing LSI chip. In: The First IEEEWorkshop on Face Processing in Video (2004)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)
Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Wang, X., Tang, X.: Random Sampling LDA for Face Recognition. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 259–265 (2004)
Watanabe, S., Pakvasa, N.: Subspace method of pattern recognition. In: Proceedings of the 1st International Joint Conference on Pattern Recognition, pp. 25–32 (1973)
Oja, E.: Subspace Methods of Pattern Recognition. Research Studies Press, England (1983)
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© 2005 Springer-Verlag Berlin Heidelberg
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Nishiyama, M., Yamaguchi, O., Fukui, K. (2005). Face Recognition with the Multiple Constrained Mutual Subspace Method. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_8
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DOI: https://doi.org/10.1007/11527923_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
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