ABAW : Facial Expression Recognition in the wild
This work addresses facial expression recognition in real-world conditions, but it is incremental as it builds on existing competition frameworks and methods.
The paper tackled the expression classification challenge in the ABAW competition by using multiple approaches, including fully supervised, semi-supervised, and noisy label methods, with results showing improvements over the baseline model by up to 10.46%.
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification challenge using multiple approaches such as fully supervised, semi-supervised and noisy label approach. Our approach using noise aware model has performed better than baseline model by 10.46% and semi supervised model has performed better than baseline model by 9.38% and the fully supervised model has performed better than the baseline by 9.34%