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Indexed by:期刊论文

Date of Publication:2011-10-01

Journal:NEUROCOMPUTING

Included Journals:Scopus、SCIE、EI

Volume:74

Issue:17

Page Number:2745-2753

ISSN No.:0925-2312

Key Words:PCA; 2DPCA; Principal components; Two dimensional principal components; Natural images

Abstract: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.