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The COVID-19 pandemic has undoubtedly changed the standards and affected allaspects of our lives, especially social communication. It has forced people toextensively wear medical face masks, in order to prevent transmission. Thisface occlusion can strongly irritate emotional reading from the face and urgesus to incorporate the whole body as an emotional cue. In this paper, we conductinsightful studies about the effect of face occlusion on emotion recognitionperformance, and showcase the superiority of full body input over the plainmasked face. We utilize a deep learning model based on the Temporal SegmentNetwork framework, and aspire to fully overcome the face mask consequences.Although facial and bodily features can be learned from a single input, thismay lead to irrelevant information confusion. By processing those featuresseparately and fusing their prediction scores, we are more effectively takingadvantage of both modalities. This framework also naturally supports temporalmodeling, by mingling information among neighboring frames. In combination,these techniques form an effective system capable of tackling emotionrecognition difficulties, caused by safety protocols applied in crucial areas.

Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective