Facial Expression Classification using Fusion of Deep Neural Network in Video for the 3rd ABAW3 Competition
This work addresses emotion recognition for human-computer interaction, but it is incremental as it builds on existing competition tasks and datasets.
The paper tackled facial expression classification in videos for the 3rd ABAW3 competition, using a transformer-based fusion method to achieve F1 scores of 30.35% on the validation set and 28.60% on the test set.
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.