Microexpression (ME) spotting is a crucial step for emotion analysis to detect people’s true emotions. However, the short duration, small motion amplitude, and limited sample number make accurate spotting and ME-locating challenging. To address these problems, we make two contributions in this article, including an ME dataset SDU2 and a spotting method. The dataset SDU2 contains hybrid expressions of 1602 video clips labeled by professional psychologists, covering six main categories of emotions with balanced distribution. Our ME spotting method is based on a magnitude- and angle-combined optical flow feature, exploiting the angle information, which has been overlooked by other spotting methods. In this method, we select four most discriminative regions of interest (ROIs) and determine the appearance of MEs using the proposed feature and a novel decision criterion based on the characteristic of the optical flow angle and magnitude. We have conducted experiments on SDU2 and CASME II datasets. The results demonstrate that our method achieves much better performance compared to other state-of-the-art methods in terms of spotting accuracy.

中文翻译：

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用于微表情点样的幅度和角度组合光流特征

微表情（ME）识别是情绪分析检测人们真实情绪的关键步骤。然而，持续时间短、运动幅度小和样本数量有限，使得精确定位和 ME 定位具有挑战性。为了解决这些问题，我们在本文中做出了两个贡献，包括 ME 数据集 SDU2 和定位方法。数据集 SDU2 包含由专业心理学家标记的 1602 个视频片段的混合表达，涵盖六大类情绪分布均衡。我们的 ME 定位方法基于幅度和角度组合的光流特征，利用角度信息，这是其他定位方法所忽略的。在这种方法中，我们选择了四个最具辨别力的感兴趣区域 (ROI)，并使用所提出的特征和基于光流角度和幅度特征的新决策标准来确定 ME 的外观。我们已经在 SDU2 和 CASME II 数据集上进行了实验。结果表明，与其他最先进的方法相比，我们的方法在定位精度方面取得了更好的性能。