Compound Expression Recognition (CER) plays a crucial role in interpersonal
interactions. Due to the existence of Compound Expressions , human emotional
expressions are complex, requiring consideration of both local and global
facial expressions to make judgments. In this paper, to address this issue, we
propose a solution based on ensemble learning methods for Compound Expression
Recognition. Specifically, our task is classification, where we train three
expression classification models based on convolutional networks, Vision
Transformers, and multi-scale local attention networks. Then, through model
ensemble using late fusion, we merge the outputs of multiple models to predict
the final result. Our method achieves high accuracy on RAF-DB and is able to
recognize expressions through zero-shot on certain portions of C-EXPR-DB.

我们提出了一种基于集成学习方法的复合表情识别的解决方案，通过训练基于卷积网络、Vision Transformers 和多尺度局部注意力网络的三个表情分类模型，通过模型集成使用后期融合的方法，我们能够在 RAF-DB 上实现高准确率，并能够通过 C-EXPR-DB 的特定部分实现零样本表情识别。