Multitask Multi-database Emotion Recognition

Understand

In this work, we introduce our submission to the 2nd Affective Behavior Analysis in-the-wild (ABAW) 2021 competition.

• We train a unified deep learning model on multi-databases to perform two tasks: seven basic facial expressions prediction and valence-arousal estimation.
• Since these databases do not contains labels for all the two tasks, we have applied the distillation knowledge technique to train two networks: one teacher and one student model.
• The student model will be trained using both ground truth labels and soft labels derived from the pretrained teacher model.