EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge
  Distillation
Maryam Nadeem Raza Imam Rouqaiah Al-Refai Meriem Chkir Mohamad Hoda Abdulmotaleb El Saddik
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.