Random sampling for subspace face recognition

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Random sampling for subspace face recognition (2006)

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by Xiaogang Wang , Xiaoou Tang

Venue:	International Journal of Computer Vision
Citations:	52 - 18 self

BibTeX

@ARTICLE{Wang06randomsampling,
    author = {Xiaogang Wang and Xiaoou Tang},
    title = {Random sampling for subspace face recognition},
    journal = {International Journal of Computer Vision},
    year = {2006},
    volume = {70},
    pages = {91--104}
}

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Abstract

Abstract. Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.

Keyphrases

subspace face recognition lda classifier training sample feature space conventional approach face recognition system key component subspace dimension subspace parameter fusion rule many case training sample set parameter selection ensemble learning framework classification system discriminative information developed random robust random training set useful discriminative information small sample size problem null space lda complementary classifier optimal parameter multiple feature feature vector high dimensional feature vector n-lda classifier gabor response single optimal subspace random subspace

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