Micro-expressions (MEs) are subtle facial movements that occur spontaneously
when people try to conceal the real emotions. Micro-expression recognition
(MER) is crucial in many fields, including criminal analysis and psychotherapy.
However, MER is challenging since MEs have low intensity and ME datasets are
small in size. To this end, a three-stream temporal-shift attention network
based on self-knowledge distillation (SKD-TSTSAN) is proposed in this paper.
Firstly, to address the low intensity of ME muscle movements, we utilize
learning-based motion magnification modules to enhance the intensity of ME
muscle movements. Secondly, we employ efficient channel attention (ECA) modules
in the local-spatial stream to make the network focus on facial regions that
are highly relevant to MEs. In addition, temporal shift modules (TSMs) are used
in the dynamic-temporal stream, which enables temporal modeling with no
additional parameters by mixing ME motion information from two different
temporal domains. Furthermore, we introduce self-knowledge distillation (SKD)
into the MER task by introducing auxiliary classifiers and using the deepest
section of the network for supervision, encouraging all blocks to fully explore
the features of the training set. Finally, extensive experiments are conducted
on four ME datasets: CASME II, SAMM, MMEW, and CAS(ME)3. The experimental
results demonstrate that our SKD-TSTSAN outperforms other existing methods and
achieves new state-of-the-art performance. Our code will be available at
this https URL

本研究提出了一种基于自我知识蒸馏的三通道时延注意力网络（SKD-TSTSAN），用于提高微表情识别的性能，并在四个微表情数据集上展开实验，结果表明 SKD-TSTSAN 优于现有方法，并取得新的最优性能。