Abstract
Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising. This paper presents our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2023 Competition for AU detection. We propose a multi-modal method for facial action unit detection with visual, acoustic, and lexical features extracted from the large pre-trained models. To provide high-quality details for visual feature extraction, we apply super-resolution and face alignment to the training data and show potential performance gain. Our approach achieves the F1 score of 52.3\% on the official validation set of the 5th ABAW Challenge.
Abstract (translated)
面部表情单元检测在面部表情分析中成为一个重要任务,旨在检测特定的、预先定义的面部表达,如唇紧收和脸颊抬起。本文介绍了我们提交的2023年野生情感行为分析竞赛(ABAW) AU检测提交。我们提出了一种多模态方法,使用从大型预训练模型中获取的视觉、声学和词向量特征进行面部表情单元检测。为了提供高质量的视觉特征提取细节,我们应用超分辨率和面部对齐训练数据,并展示了潜在的性能提升。我们的方法在官方验证集上的F1得分为52.3%。
URL
https://arxiv.org/abs/2303.10590
