Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions
Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions
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DOI：
10.1109/cvprw56347.2022.00277
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Ankith Jain Rakesh Kumar;B. Bhanu
中科院分区：
文献类型：
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作者：
Ankith Jain Rakesh Kumar;B. Bhanu
为了了解人类在社交互动中表达的真实情感，有必要识别个体面部表现出的微妙变化（微表情）。面部微表情是短暂、快速、自发的手势和皮肤下的非自愿面部肌肉运动。因此，对面部微表情进行分类是一项具有挑战性的任务。本文提出了一种端到端新颖的三流图注意网络模型，通过利用光流大小、光流方向和节点位置特征之间的关系来捕获面部的细微变化并识别微表情（ME）。面部图表示结构用于使用三个帧提取空间和时间信息。光流特征的变化的动态块大小用于提取每个标志点的局部纹理信息。该网络仅利用地标点的位置特征和这些点上的光流信息，并为 ME 的分类产生良好的结果。对 SAMM 和 CASME II 数据集的综合评估证明了该方法的高效性、高效性和通用性，并取得了比最先进方法更好的结果。
To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.