Micro-expressions are the rapid movements of facial muscles that can be used to reveal concealed emotions. Recognizing them from video clips has a wide range of applications and receives increasing attention recently. Among existing methods, the main directional mean optical-flow (MDMO) feature achieves state-of-the-art performance for recognizing spontaneous micro-expressions. For a video clip, the MDMO feature is computed by averaging a set of atomic features frame-by-frame. Despite its simplicity, the average operation in MDMO can easily lose the underlying manifold structure inherent in the feature space. In this paper we propose a sparse MDMO feature that learns an effective dictionary from a micro-expression video dataset. In particular, a new distance metric is proposed based on the sparsity of sample points in the MDMO feature space, which can efficiently reveal the underlying manifold structure. The proposed sparse MDMO feature is obtained by incorporating this new metric into the classic graph regularized sparse coding (GraphSC) scheme. We evaluate sparse MDMO and four representative features (LBP-TOP, STCLQP, MDMO and FDM) on three spontaneous micro-expression datasets (SMIC, CASME and CASME II). The results show that sparse MDMO outperforms these representative features.

中文翻译：

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稀疏 MDMO：学习用于自发微表情识别的判别特征

微表情是面部肌肉的快速运动，可以用来揭示隐藏的情绪。从视频片段中识别它们具有广泛的应用，并且最近受到越来越多的关注。在现有方法中，主要的方向平均光流 (MDMO) 特征在识别自发微表情方面达到了最先进的性能。对于视频剪辑，MDMO 特征是通过逐帧平均一组原子特征来计算的。尽管它很简单，但 MDMO 中的平均操作很容易丢失特征空间中固有的底层流形结构。在本文中，我们提出了一种稀疏 MDMO 特征，该特征可以从微表情视频数据集中学习有效字典。特别地，基于MDMO特征空间中样本点的稀疏性提出了一种新的距离度量，这可以有效地揭示潜在的流形结构。提出的稀疏 MDMO 特征是通过将这一新度量纳入经典的图正则化稀疏编码 (GraphSC) 方案而获得的。我们在三个自发微表达数据集（SMIC、CASME 和 CASME II）上评估稀疏 MDMO 和四个代表性特征（LBP-TOP、STCLQP、MDMO 和 FDM）。结果表明，稀疏 MDMO 优于这些代表性特征。