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论文类型：期刊论文

发表时间：2011-10-01

发表刊物：NEUROCOMPUTING

收录刊物：Scopus、SCIE、EI

卷号：74

期号：17

页面范围：2745-2753

ISSN号：0925-2312

关键字：PCA; 2DPCA; Principal components; Two dimensional principal components; Natural images

摘要：In this paper, two dimensional principal components of natural images (2D-PCs) are proposed. Similar to principal components of natural images (1D-PCs), 2D-PCs can also be viewed as fundamental components of human's receptive field because they contain edge-like, bar-like and grating-like patterns. However, compared to 1D-PCs, 2D-PCs are of surprising symmetry, stable regularity, good interpretability, and have little computational complexity in real applications. Then, based on 1D-PCs and 2D-PCs, we design two kinds of statistical texture features (STF(1D) and STF(2D)), and apply them to multi-class facial expression recognition. Numerous experimental results demonstrate that our statistical texture features are better or not worse than other popular features for facial expression recognition. (C) 2011 Elsevier B.V. All rights reserved.