Two Dimensional Principal Components of Natural Images and Its Application
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| Authors | Dong Wang, Huchuan Lu, Xuelong Li |
| Journal/Conference Name | NEUROCOMPUTING |
| Paper Category | ECE |
| Paper 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. |
| Date of publication | 2011 |
| Code Programming Language | MATLAB |
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