论文标题
面部表达分类使用深度神经网络在视频中的第三次ABAW3竞赛中的融合
Facial Expression Classification using Fusion of Deep Neural Network in Video for the 3rd ABAW3 Competition
论文作者
论文摘要
对于计算机识别人类情绪，表达分类是人类计算机相互作用领域同样重要的问题。在第三次情感行为分析中，表达式分类的任务包括八个类，其中有六个来自视频中人类面孔的基本表达式。在本文中，我们采用了变压器机制来编码主链的鲁棒表示。稳健表示的融合在表达分类任务中起重要作用。我们的方法分别在验证集和测试集上的$ f_1 $得分达到30.35 \％和28.60 \％。该结果显示了基于AFF-WILD2数据集的拟议体系结构的有效性。
For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35\% and 28.60\% for the $F_1$ score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset.
