关键词: 表情识别, Gabor小波, 二值叠加中心对称局部二值模式, 离散余弦变换, 降维

Abstract: In facial expression recognition, the feature extraction methods such as Gabor combined with Local Binary Pattern （LBP） and histograms for dimension reduction are more localization methods, but the LBP is of poor robustness and low precision for recognition, and the computational complexity and the dimension of characteristics are still high for histograms. Compared with LBP, Center-Symmetric Local Binary Pattern （CS-LBP） is more robust, but its description for facial expression texture is insufficiently detailed. This paper proposes a new feature extraction method based on Gabor combined with improved CS-LBP. The improved CS-LBP is addition of two-valued center-symmetric local binary pattern. The method uses Gabor to extract feature, and the final recognition feature is the superposition of two different features calculated by two methods. The dimensions of the final recognition feature are effectively reduced by Discrete Cosine Transform （DCT）. The proposed method is tested by using JAFFE facial expression database. The result shows that the method can effectively improve the recognition accuracy.

Key words: facial expression recognition, Gabor wavelet, addition of two-valued center-symmetric local binary pattern, discrete cosine transform, feature dimension reduction