  As virtual environments continue to advance, the demand for immersive and
emotionally engaging experiences has grown. Addressing this demand, we
introduce Emotion enabled Virtual avatar mapping using Optimized KnowledgE
distillation (EVOKE), a lightweight emotion recognition framework designed for
the seamless integration of emotion recognition into 3D avatars within virtual
environments. Our approach leverages knowledge distillation involving
multi-label classification on the publicly available DEAP dataset, which covers
valence, arousal, and dominance as primary emotional classes. Remarkably, our
distilled model, a CNN with only two convolutional layers and 18 times fewer
parameters than the teacher model, achieves competitive results, boasting an
accuracy of 87% while demanding far less computational resources. This
equilibrium between performance and deployability positions our framework as an
ideal choice for virtual environment systems. Furthermore, the multi-label
classification outcomes are utilized to map emotions onto custom-designed 3D
avatars.
