多尺度中心误差补偿二值模式的表情识别

针对Gabor小波与局部二值模式（Local Binary Pattern，LBP）在表情识别上的局限性，提出了一种多尺度中心误差补偿二值模式（Center Error Compensation Binary Pattern，CECBP）的表情识别方法。对预处理后的人脸表情图像创建多尺度的金字塔，用中心误差补偿二值模式对金字塔中的各层图像进行编码，分块提取各层编码后的直方图序列作为特征，用支持向量机（Support Vector Machine，SVM）进行分类。在JAFFE、Cohn-Kanade以及Pain Expression表情库上的交叉验证表明，该方法可以抑制噪声，具有较高的识别率和较快的识别速度，比传统的Gabor小波以及LBP更具有优势。

Facial expression recognition based on multi-scale center error compensation binary pattern

In order to solve the limitations of Gabor wavelet and Local Binary Pattern（LBP） in facial expression recognition, a method based on multi-scale Center Error Compensation Binary Pattern（CECBP） is proposed. In the method, the images are preprocessed and multi-scale pyramid of these images are then created. Center Error Compensation Binary Pattern（CECBP） is utilized to encode the images of every layer in image pyramid. The chunked and encoded histogram sequences are used as a feature and Support Vector Machine（SVM） is used in classification. To demonstrate the superiority of the proposed method over traditional Gabor wavelet and LBP, cross-validations on JAFFE, Cohn-Kanade and Pain expression database, it shows that the method can not only suppress noise, but also have state-of-the-art classification accuracy and faster recognition speed.