Multi-term and Multi-task Affect Analysis in the Wild

Human affect recognition is an important factor in human-computer interaction. However, the development of method for in-the-wild data is still nowhere near enough. in this paper, we introduce the affect recognition method that was submitted to the Affective Behavior Analysis in-the-wild (ABAW) 2020 Contest. In our approach, since we considered that affective behaviors have different time window features, we generated features and averaged labels using short-term, medium-term, and long-term time windows from video images. Then, we generated affect recognition models in each time window, and esembled each models. In addition,we fuseed the VA and EXP models, taking into account that Valence, Arousal, and Expresion are closely related. The features were trained by gradient boosting, using the mean, standard deviation, max-range, and slope in each time winodows. We achieved the valence-arousal score: 0.495 and expression score: 0.464 on the validation set.