论文标题
SMA-STN：分段的运动时空网络fomicro-Expression识别
SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition
论文作者
论文摘要
正确地感知微表达是困难的，因为微表达是一种非自愿，压抑和微妙的面部表达，并且有效地揭示了微妙的运动变化并捕获微观表达序列中的显着片段是微表达识别（MER）的关键。为了解决关键问题，在本文中，我们首先提出了一个动态分段的稀疏成像模块（DSSI），以将动态图像作为局部 - 全球时机时空描述符，在独特的采样协议下，以有效的方式揭示了微妙的运动在视觉上的变化。其次，提出了分段的运动时空网络（SMA-STN），以进一步揭示不可察觉的小运动变化，该运动利用时空移动的模块（STMA）来捕获长距离的空间关系，以实现面部表达和称重临时片段。此外，将偏差增强损失（DE-loss）嵌入SMA-STN中，以增强SMA-STN对特征水平的细微运动变化的鲁棒性。对三种广泛使用的基准（即Casme II，SAMM和SHIC）进行的广泛实验表明，所提出的SMA-STN比其他最先进的方法实现了更好的MER性能，这证明该方法证明该方法有效地解决了具有挑战性的MER问题。
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.
