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Human affective recognition is an important factor in human-computerinteraction. However, the method development with in-the-wild data is not yetaccurate enough for practical usage. In this paper, we introduce the affectiverecognition method focusing on valence-arousal (VA) and expression (EXP) thatwas submitted to the Affective Behavior Analysis in-the-wild (ABAW) 2020Contest. Since we considered that affective behaviors have many observablefeatures that have their own time frames, we introduced multiple optimized timewindows (short-term, middle-term, and long-term) into our analyzing frameworkfor extracting feature parameters from video data. Moreover, multiple modalitydata are used, including action units, head poses, gaze, posture, and ResNet 50or Efficient NET features, and are optimized during the extraction of thesefeatures. Then, we generated affective recognition models for each time windowand ensembled these models together. Also, we fussed the valence, arousal, andexpression models together to enable the multi-task learning, considering thefact that the basic psychological states behind facial expressions are closelyrelated to each another. In the validation set, our model achieved avalence-arousal score of 0.498 and a facial expression score of 0.471. Theseverification results reveal that our proposed framework can improve estimationaccuracy and robustness effectively.

A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wild