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Spotting Rapid Facial Movements from Videos Using Appearance-Based Feature Difference Analysis

机译:使用基于外观的特征差异分析从视频中识别快速的面部运动

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Spotting micro-expressions is a primary step for continuous emotion recognition from videos. Spotting in this context refers to automatically finding the temporal locations of the face-related events from a video sequence. Rapid facial movements mainly include micro-expressions and eye blinks. However, the role of eye blinks in expressing emotions is still controversial, and often they are considered as micro-expressions as well. In this paper a simple method for automatically spotting rapid facial movements from videos is proposed. The method relies on analyzing differences in appearance-based features of sequential frames. In addition to finding the temporal locations, the system is able to provide spatial information about the movements in the face. Micro-expression spotting experiments are carried out on three datasets consisting only of spontaneous micro-expressions. Baseline micro-expression spotting results are provided for these three datasets including the publicly available CASME database. Also an example of spatial localization of the spotted rapid movements is presented.
机译:识别微表情是从视频中连续识别情感的主要步骤。在这种情况下,点播是指从视频序列中自动找到与面部相关的事件的时间位置。快速的面部运动主要包括微表情和眨眼。但是,眨眼在表达情绪中的作用仍存在争议,通常也被视为微表情。在本文中,提出了一种从视频中自动发现快速面部移动的简单方法。该方法依赖于分析顺序帧的基于外观的特征中的差异。除了找到时间位置之外,该系统还能够提供有关面部运动的空间信息。微表达斑点实验是在仅由自发微表达组成的三个数据集上进行的。为这三个数据集(包括可公开获得的CASME数据库)提供了基线微表达发现结果。还提出了斑点快速运动的空间定位的示例。

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