Ensemble LDA for face recognition

Authors:
Hui Kong;Xuchun Li;Jian-Gang Wang;Chandra Kambhamettu
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;Institute for Infocomm Research, Singapore;Department of Computer and Information Science, University of Delaware, Newark, DE
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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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 (EnLDA) is proposed for face recognition with small number of training samples. In EnLDA, 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 EnLDA over the state-of-the-art algorithms in recognition accuracy.