Bagging null space locality preserving discriminant classifiers for face recognition

Authors:
Liping Yang;Weiguo Gong;Xiaohua Gu;Weihong Li;Yanfei Liu
Affiliations:
Key Laboratory of Opto-Electronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400030, China;Key Laboratory of Opto-Electronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400030, China;Key Laboratory of Opto-Electronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400030, China;Key Laboratory of Opto-Electronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400030, China;Key Laboratory of Opto-Electronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400030, China
Venue:
Pattern Recognition
Year:
2009

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Abstract

In this paper, we propose a novel bagging null space locality preserving discriminant analysis (bagNLPDA) method for facial feature extraction and recognition. The bagNLPDA method first projects all the training samples into the range space of a so-called locality preserving total scatter matrix without losing any discriminative information. The projected training samples are then randomly sampled using bagging to generate a set of bootstrap replicates. Null space discriminant analysis is performed in each replicate and the results of them are combined using majority voting. As a result, the proposed method aggregates a set of complementary null space locality preserving discriminant classifiers. Experiments on FERET and PIE subsets demonstrate the effectiveness of bagNLPDA.