Abstract

Obtaining a useful and discriminative feature for facial expression recognition (FER) is a hot research topic in computer vision. In this paper, we propose a novel facial expression representation for FER. Firstly, we select the appropriate parameter of multi-scale block local binary pattern uniform histogram (MB-LBPUH) operator to filter the facial images for representing the holistic structural features. Then, normalizing the filtered images into a uniform basis reduces the computational complexity and remains the full information. An MB-LBPUH feature and a HOG feature are concatenated to fuse a new feature representation for characterizing facial expressions. At the same time, weighting the MB-LBPUH feature can remove the data unbalance from a fusion feature. The weighted-fusion feature reflects not only global facial expressions structure patterns but also characterizes local expression texture appearance and shape. Finally, we utilize principal component analysis for dimensionality reduction and employ support vector machine to classification. Experimental results demonstrate that the proposed algorithm exhibits superior performance compared with the existing algorithms on JAFFE, CK+, and BU-3DFE datasets.

中文翻译：

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MB-LBPUH和HOG的加权融合功能用于面部表情识别

摘要

获得有用且有区别的面部表情识别（FER）功能是计算机视觉中的一个热门研究主题。在本文中，我们提出了一种新颖的FER面部表情表示方法。首先，我们选择多尺度块局部二值模式均匀直方图（MB-LBPUH）运算符的适当参数来过滤代表整体结构特征的面部图像。然后，将滤波后的图像归一化为统一的基础可以降低计算复杂度并保留完整的信息。结合了MB-LBPUH功能和HOG功能以融合用于表征面部表情的新功能表示。同时，对MB-LBPUH功能进行加权可以消除融合功能中的数据不平衡。加权融合功能不仅反映了全局面部表情的结构模式，而且还表征了局部表情纹理的外观和形状。最后，我们利用主成分分析进行降维，并使用支持向量机进行分类。实验结果表明，在JAFFE，CK +和BU-3DFE数据集上，与现有算法相比，该算法具有更好的性能。