关键词: 表情识别, 格雷码模式, 稀疏表示

Abstract: The traditional LBP feature extraction of the image is sensitive to the change of the non-monotonic light. The global feature can’t be sparsely expressed by the LBP. An adaptive weighted Local Gray Code Patterns（LGCP） and fast sparse representation of feature extraction methods is proposed. The edge detection operator is used to maximize the edge values of the original image to overcome the influence of feature description from the light changes. Eight bit gray code is got by using LGCP and is converted into decimal. The optimal representation of local features will be got by the weighted cascade block. Distribution characteristics descriptor of the cascade histogram is as the atoms to form the dictionary. The global feature of the image would have better sparse representation. Finally, a fast sparse representation is selected as a classifier for classification. Several experiments on the extended Cohn-Kanade(CK+) expression data set show that the method has a rapid recognition, and the recognition rate is up to 94%.

Key words: expression recognition, Gray Code Pattern(GCP), sparse representation