Abstract: Aiming at the problem that Local Binary Pattern（LBP） algorithm ignores the local structure information when extracting texture features, a novel expression recognition algorithm by adaptive weighted fusion of salient structure tensor and LBP feature is proposed. The method first eliminates fine textures and noises by detecting the salient regions on the entire image to obtain a global salient map. Second, salient texture features are further extracted based on the salient map. According to the contribution of each feature information entropy, the feature vector is weighted. Finally, the Support Vector Machine（SVM） is used to classify the expression images. The experimental results show that the two texture features of adaptive weighted fusion can better describe the features of human face and effectively improve the expression recognition rate.

Key words: salient map, structure tensor feature, Local Binary Pattern（LBP） feature, adaptive weighted

摘要： 针对局部二值模式（Local Binary Pattern，LBP）提取纹理特征时忽略了图像的局部结构信息问题，提出一种自适应加权融合显著性结构张量和LBP的表情识别算法。该算法通过对整幅图片进行显著性区域检测得到全局显著图来消除细小的纹理和噪声。在显著图的基础上进一步提取两种显著性纹理特征，根据每种特征信息熵的贡献度来作为特征向量的加权依据。利用支持向量机（Support Vector Machine，SVM）进行表情图像的分类。实验结果表明，自适应加权融合的两种纹理特征能够较好地描述人脸的特征，有效地提高表情识别率。

关键词: 显著图, 结构张量特征, 局部二值模式（LBP）特征, 自适应加权