Facial Affective Behavior Analysis Method for 5th ABAW Competition
This work is incremental, focusing on improving performance for specific challenges in a competition setting, relevant to researchers in affective computing and human-computer interaction.
The paper tackled three facial affective behavior analysis tasks (valence-arousal estimation, expression classification, action unit recognition) from the 5th ABAW competition using the Aff-Wild2 database, constructing separate models to address issues like data imbalance and noise, but no concrete results or numbers were reported.
Facial affective behavior analysis is important for human-computer interaction. 5th ABAW competition includes three challenges from Aff-Wild2 database. Three common facial affective analysis tasks are involved, i.e. valence-arousal estimation, expression classification, action unit recognition. For the three challenges, we construct three different models to solve the corresponding problems to improve the results, such as data unbalance and data noise. For the experiments of three challenges, we train the models on the provided training data and validate the models on the validation data.