Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective
This addresses emotion recognition challenges in social communication for public health and safety applications, but it is incremental as it builds on existing deep learning frameworks.
The paper tackles the problem of emotion recognition when faces are occluded by medical masks during the COVID-19 pandemic, showing that using full body input outperforms masked face input, with a deep learning model achieving improved performance by separately processing and fusing facial and bodily features.
The COVID-19 pandemic has undoubtedly changed the standards and affected all aspects of our lives, especially social communication. It has forced people to extensively wear medical face masks, in order to prevent transmission. This face occlusion can strongly irritate emotional reading from the face and urges us to incorporate the whole body as an emotional cue. In this paper, we conduct insightful studies about the effect of face occlusion on emotion recognition performance, and showcase the superiority of full body input over the plain masked face. We utilize a deep learning model based on the Temporal Segment Network framework, and aspire to fully overcome the face mask consequences. Although facial and bodily features can be learned from a single input, this may lead to irrelevant information confusion. By processing those features separately and fusing their prediction scores, we are more effectively taking advantage of both modalities. This framework also naturally supports temporal modeling, by mingling information among neighboring frames. In combination, these techniques form an effective system capable of tackling emotion recognition difficulties, caused by safety protocols applied in crucial areas.