  Current works in human emotion recognition follow the traditional closed
learning approach governed by rigid rules without any consideration of novelty.
Classification models are trained on some collected datasets and expected to
have the same data distribution in the real-world deployment. Due to the fluid
and constantly changing nature of the world we live in, it is possible to have
unexpected and novel sample distribution which can lead the model to fail.
Hence, in this work, we propose a continuous learning based approach to deal
with novelty in the automatic emotion recognition task.
