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期刊论文
Bagging null space locality preseving discriminant classifiers for face recognition
Pattern Recognition,-0001,():
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.
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