  The current COVID-19 pandemic has shown us that we are still facing
unpredictable challenges in our society. The necessary constrain on social
interactions affected heavily how we envision and prepare the future of social
robots and artificial agents in general. Adapting current affective perception
models towards constrained perception based on the hard separation between
facial perception and affective understanding would help us to provide robust
systems. In this paper, we perform an in-depth analysis of how recognizing
affect from persons with masks differs from general facial expression
perception. We evaluate how the recently proposed FaceChannel adapts towards
recognizing facial expressions from persons with masks. In Our analysis, we
evaluate different training and fine-tuning schemes to understand better the
impact of masked facial expressions. We also perform specific feature-level
visualization to demonstrate how the inherent capabilities of the FaceChannel
to learn and combine facial features change when in a constrained social
interaction scenario.
