Facial expression recognition (FER) has received increasing interest in
computer vision. We propose the TransFER model which can learn rich
relation-aware local representations. It mainly consists of three components:
Multi-Attention Dropping (MAD), ViT-FER, and Multi-head Self-Attention Dropping
(MSAD). First, local patches play an important role in distinguishing various
expressions, however, few existing works can locate discriminative and diverse
local patches. This can cause serious problems when some patches are invisible
due to pose variations or viewpoint changes. To address this issue, the MAD is
proposed to randomly drop an attention map. Consequently, models are pushed to
explore diverse local patches adaptively. Second, to build rich relations
between different local patches, the Vision Transformers (ViT) are used in FER,
called ViT-FER. Since the global scope is used to reinforce each local patch, a
better representation is obtained to boost the FER performance. Thirdly, the
multi-head self-attention allows ViT to jointly attend to features from
different information subspaces at different positions. Given no explicit
guidance, however, multiple self-attentions may extract similar relations. To
address this, the MSAD is proposed to randomly drop one self-attention module.
As a result, models are forced to learn rich relations among diverse local
patches. Our proposed TransFER model outperforms the state-of-the-art methods
on several FER benchmarks, showing its effectiveness and usefulness.

提出 TransFER 模型，在面部表情识别中使用多头自注意力且随机选择模块，有效地学习富含关系感知的局部表征，实验结果显示其性能优于现有方法。