SMA-STN：分段运动参与时空网络forMicro-Expression识别
SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition
摘要：
由于微表情是一种无意识的、压抑的、微妙的面部表情，因此正确地感知微表情是困难的，有效地揭示微表情序列中的细微动作变化并捕捉其中的重要片段是微表情识别（MER）的关键。为了解决这一关键问题，本文首先提出了一个动态分段稀疏成像模块（DSSI），在一个独特的采样协议下计算动态图像作为局部-全局时空描述符，以有效的方式直观地揭示了细微的运动变化。其次，提出了一种分段运动感知时空网络（SMA-STMA），利用时空运动感知模块（STMA）捕捉面部表情的长距离空间关系，并对时间段进行加权，进一步揭示细微的运动变化。此外，偏差增强损失（DE-Loss）被嵌入到SMA-SVM中，以增强SMA-SVM对特征级细微运动变化的鲁棒性。在三个广泛使用的基准上进行了广泛的实验，即，CASME II，SAMM和SHIC，表明所提出的SMA-TORCH实现了更好的MER性能比其他国家的最先进的方法，这证明了所提出的方法是有效的，以处理具有挑战性的MER问题。
英文摘要：
Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER). To handle the crucial issue, in this paper, we firstly propose a dynamic segmented sparse imaging module (DSSI) to compute dynamic images as local-global spatiotemporal descriptors under a unique sampling protocol, which reveals the subtle movement changes visually in an efficient way. Secondly, a segmented movement-attending spatiotemporal network (SMA-STN) is proposed to further unveil imperceptible small movement changes, which utilizes a spatiotemporal movement-attending module (STMA) to capture long-distance spatial relation for facial expression and weigh temporal segments. Besides, a deviation enhancement loss (DE-Loss) is embedded in the SMA-STN to enhance the robustness of SMA-STN to subtle movement changes in feature level. Extensive experiments on three widely used benchmarks, i.e., CASME II, SAMM, and SHIC, show that the proposed SMA-STN achieves better MER performance than other state-of-the-art methods, which proves that the proposed method is effective to handle the challenging MER problem.
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