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Facial Expression Recognition Based on Local Vector Model

机译：基于局部矢量模型的面部表情识别

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摘要

Texture feature extraction is an important step in the facial expression recognition system. The traditional LBP method ignored the statistical characteristics of the texture change direction in the process of feature extraction, and we can extract more detailed texture information by the LDP method based on LBP, but the computational complexity is greatly increased. In order to extract more detailed texture information with the computational complexity is not increased, we proposed a method named Local Vector Model (LVM). In this method, modulus value and direction of the local texture changes are extracted as the features of classification. Furthermore, in order to improve the robustness that the algorithm to the subtle deformation of expression image, the Image Euclidean Distance is introduced and embedded in LVM. Finally, the even decreasing function is used to get the neighbor classification distance. Experiments on JAFFE facial expression databases with different resolution demonstrated that the method proposed in this paper is better than other modern methods.
机译：纹理特征提取是面部表情识别系统中的重要步骤。传统的LBP方法在特征提取过程中忽略了纹理变化方向的统计特性，通过基于LBP的LDP方法可以提取出更详细的纹理信息，但计算复杂度大大提高。为了在不增加计算复杂度的情况下提取更详细的纹理信息，我们提出了一种称为局部矢量模型（LVM）的方法。在这种方法中，模量值和局部纹理变化的方向被提取为分类的特征。此外，为了提高算法对表情图像细微变形的鲁棒性，引入了图像欧几里得距离并将其嵌入到LVM中。最后，使用偶数递减函数获得邻居分类距离。在不同分辨率的JAFFE面部表情数据库上的实验表明，本文提出的方法优于其他现代方法。

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