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.

本篇论文提出动态分段稀疏成像模块（DSSI）和分段运动关注空间时间网络（SMA-STN）来处理微表情识别（MER）问题，通过计算局部 - 全局的时空描述符来捕捉微小运动变化，利用空 - 时运动关注模块（STMA）来捕捉面部表情的远程空间关系和加权时间段，实验证明该方法优于其他先进方法，证明该方法是解决复杂的 MER 问题有效的。