Abstract
Boosting is a general method for improving the performance of any learning algorithm. In this paper, an unusual regularization technique, boosting in random subspace, is employed to improve generalization capability of boosting. Meanwhile the space complexity of training is lowered, and the classifier combination strategy can be used to further improve recognition accuracy. Using the method, we achieved 98.99% rank-1 recognition rate on FERET fb probe set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jones, M., Viola, P.: Face Recognition Using Boosted Local Features. MERL Technical Reports 25 (2003)
Yang, P., Shan, S., Gao, W., Li, S.Z., Zhang, D.: Face Recognition Using Ada-Boosted Gabor Features. FGR (2004)
Zhang, L., Li, S.Z., ZhiYi Qu, Huang, X.: Boosting Local Feature Based Classifiers For Face Recognition. CVPR Workshops (2004)
Zhang, G., Huang, X., Z.Li, S., Wang, Y., Wu, X.: Boosting Local Binary Pattern (LBP)-Based Face Recognition. Sinobiometrics’04 (2004)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition With Local Binary Patterns. ECCV (2004) 469–481
Vapnik, V.N., Chervonenkis, A.Y.: On The Uniform Convergence Of Relative Frequencies Of Events To Their Probabilites (1971) Theory of Probability and its applications.
P. J. Pillips, H. Moon, S.A.R., Rauss, P.J.: The Feret Evaluation Methodology For Face Recognition Algorithms. IEEE Trans. on PAMI 22 (2000)
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 (1997) 119–139
Freund, Y.: An Introduction To Boosting Based Classification. Proceedings of the AT&T conference on Quantitative Analysis (1998)
Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Prediction. Proceeding of the Eleventh Annual Conference on Computation Learning Theory (1998) 80–91
Schapire, R.E., Freund, Y., Bartlett, P., Lee, W.S.: Boosting The Margin: A New Explanation For The Effectiveness Of Voting Methods. The Annals of Statistics 26 (1998) 1651–1686
Grove, A., Schuurmans, D.: Boosting in the Limit: Maximizing the Margin of Learned Ensembles. Proceedings of the Fifteenth National Conference on Artificial Intelligence (1998)
Ho, T.K.: Random Decision Forests. Proc. Third Int’l Conf. Document Analysis and Recognition (1995) 278–282
Kleinberg, E.M.: An Overtraining-resistant Stochastic Modeling Method for Pattern Recognition. Annals of Statistics 4 (1996) 2,319–2,349
Viola, P., Jones, M.: Robust Real Time Object Detection. ICCV (2001)
Gao, Y., Wang, Y.: Boosting in Random Subspaces for Face Recognition. Accepted by ICPR06 (2006)
Gao, Y., Wang, Y., Feng, X., Zhou, X.: Boosting Gabor Features For Face Recognition Using Random Subspace. Accepted by ICASSP06 (2006)
Belhumeur, P.N., ao P. Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on PAMI 19 (1997)
Liu, C., Wechsler, H.: Gabor Feature Based Classification Using The Enhanced Fisher Linear Discriminant Mdoel for Face Recognition. IEEE Trans. Image Processing 11 (2002) 467–476
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-statistical Model For Face Representation And Recognition. ICCV (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Gao, Y., Wang, Y. (2006). Boosting in Random Subspace for Face Recognition. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-37258-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
eBook Packages: EngineeringEngineering (R0)