融合小波和LBP-GD特征的人脸表情识别

针对局部二值模式（LBP）不能描述纹理方向变化的问题，提出了一种融合了梯度方向的LBP-GD算子。LBP-GD算子不仅保持了LBP本身的优点，还可以细致刻画纹理的方向信息。由于人脸表情器官所蕴含信息的差异性，设计了一种不规则的分块方式，把图像分为9个互不重叠的子块并且设置不同的权值系数，然后提取每个子块的LBP-GD特征。最后，将LBP-GD特征与提升小波的低频分量特征加权融合，用K近邻方法进行分类。在JAFFE和Cohn-Kanade表情库上验证了该方法的有效性。实验结果表明，该方法比单独使用LBP-GD特征和提升小波特征具有更好的识别效果。

Feature Fusion of Wavelet and LBP-GD for Facial Expression Recognition

Aiming at the problem that local binary pattern (LBP) cannot describe the change of texture direction, an LBP-gradient direction (LBP-GD) operator which combines gradient direction with LBP is proposed. LBP-GD operator not only keeps the advantages of LBP, but also describes texture direction in detail. Due to the difference of information contained in facial expression organs, an irregular dividing method is designed. The image is divided into 9 non-overlapping sub-blocks with different weighted values, then LBP-GD feature of each sub-block is extracted. Finally, the LBP-GD feature is weightily fused with low-frequency components obtained by lifting wavelet (LW) transform, and the K-nearest neighbor classifier is applied for expression classification. The effectiveness of this approach has been demonstrated on the JAFFE and Cohn-Kanade facial expression databases. Experimental results show that the proposed method achieves better performance than LW and LBP-GD feature alone.