Abstract
Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1][2][3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing the overfitting problem for the two-step LDA approach, a framework of Ensemble Linear Discriminant Analysis (E n LDA) is proposed for face recognition with small number of training samples. In E n LDA, a Boosting-LDA (B-LDA) and a Random Sub-feature LDA (RS-LDA) schemes are incorporated together to construct the total weak-LDA classifier ensemble. By combining these weak-LDA classifiers using majority voting method, recognition accuracy can be significantly improved. Extensive experiments on two public face databases verify the superiority of the proposed E n LDA over the state-of-the-art algorithms in recognition accuracy.
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Keywords
- Training Sample
- Face Recognition
- Linear Discriminant Analysis
- Recognition Accuracy
- Discriminative Information
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References
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on PAMI 19, 711–720 (1997)
Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 831–836 (1996)
Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new lda-based face recognition system which can solve the samll sample size problem. Pattern Recognition (2000)
Fukunnaga: Introduction to Statistical Pattern Recognition. Academic Press, New York (1991)
Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)
Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the small sample size problem of lda. In: Proceedings of International Conference on Pattern Recognition (2002)
Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 4–13 (2005)
Wang, X., Tang, X.: Random sampling lda for face recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)
Wang, X., Tang, X.: Dual-space linear discriminant analysis for face recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)
Kong, H., Wang, L., Teoh, E., Wang, J., Venkateswarlu, R.: A framework of 2d fisher discriminant analysis: Application to face recognition with small number of training samples. To appear in the IEEE International Conference on Computer Vision and Pattern Recognition 2005 (2005)
Breima, L.: Bagging predictors. Machine Learning 10, 123–140 (1996)
Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37, 297–336 (1999)
Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)
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Kong, H., Li, X., Wang, JG., Kambhamettu, C. (2005). Ensemble LDA for Face Recognition. In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics. ICB 2006. Lecture Notes in Computer Science, vol 3832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11608288_23
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DOI: https://doi.org/10.1007/11608288_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31111-9
Online ISBN: 978-3-540-31621-3
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