We present a facial emotion recognition framework, built upon Swin vision Transformers jointly with squeeze and excitation block (SE). A transformer model based on an attention mechanism has been presented recently to address vision tasks. Our method uses a vision transformer with a Squeeze excitation block (SE) and sharpness-aware minimizer (SAM). We have used a hybrid dataset, to train our model and the AffectNet dataset to evaluate the result of our model

翻译：我们提出了一个面部情感识别框架,这个框架以Swin视觉变形器为基础,与挤压和刺激区(SE)联合建立。最近提出了一种基于关注机制的变压器模型,用于应对视觉任务。我们的方法使用一个带有震动感应区(SE)和锐利感知最小化器(SAM)的视觉变压器。我们使用了一个混合数据集,来培训我们的模型和AffectNet数据集来评估模型的结果。