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首页> 外文会议>International conference on advanced concepts for intelligent vision systems >Facial Expression Recognition Using Local Region Specific Dense Optical Flow and LBP Features
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Facial Expression Recognition Using Local Region Specific Dense Optical Flow and LBP Features

机译：使用局部区域密集光流和LBP特征的面部表情识别

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摘要

Recognition of facial expression has many applications including human-computer interaction, human emotion analysis, personality development, cognitive science, health-care, virtual reality, image retrieval, etc. In this paper we propose a new method for recognition of facial expression using local region specific mean optical flow and local binary pattern feature descriptor with support vector machine classification. In general, facial expression recognition techniques divide the face into regular grid (holistic representation) and the facial features are extracted. However, in this paper we divide the face into domain specific local regions. At first a robust optical flow is utilized to get mean optical flow in different directions for each local region which considers both local statistic motion information and its spatial location. The features are used only from the key frames; which are detected based on maximal mean optical flow magnitude within a sequence w.r.t. neutral frame. Now, the region specific local binary pattern is extracted from key frame and concatenated with mean optical flow features. The performance of the proposed facial expression recognition system has been validated on CK+ facial expression dataset.
机译：面部表情的识别具有许多应用，包括人机交互，人类情感分析，人格发展，认知科学，医疗保健，虚拟现实，图像检索等。在本文中，我们提出了一种新的使用局部图像进行面部表情识别的方法支持向量机分类的区域特定平均光流和局部二进制模式特征描述符。通常，面部表情识别技术将面部分为规则网格（整体表示），并提取面部特征。但是，在本文中，我们将脸部划分为特定领域的局部区域。首先，利用鲁棒的光流来获取每个局部区域在不同方向上的平均光流，该平均光流同时考虑了局部统计运动信息及其空间位置。这些功能仅在关键帧中使用。它们是根据序列w.r.t中的最大平均光流大小来检测的。中性框架。现在，从关键帧中提取区域特定的本地二进制模式，并将其与平均光流特征相连接。所提出的面部表情识别系统的性能已在CK +面部表情数据集上得到验证。

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