Individuality and User-Specific Approach in Adaptive Emotion Recognition Model

This study aims at developing an intelligent agent that can recognize user-specific emotions and can self-evolve. Previous studies have explored several methods to develop the model and improve the results while maintaining the feasibility of real-time implementation for later stages. We evolved the emotion recognition module by using Genetic Programming (GP) and explored several optimizations. We investigated and compared the evolution of a unique classifier (evolved from data from a single specific subject only), the evolution of a general classifier (evolved from data from multiple subjects), and the evolution of an adaptive classifier by implementing incremental GP (evolved incrementally, first from multiple subjects and then from a single specific subject). We conducted the experiments by using the same budget in terms of evolution sessions to obtain the best programs for a fair comparison between general approach, user-specific approach, and adaptive approach. We then performed repeated experiments to verify the robustness of the method. From the results, we concluded that, on an average, adaptive approach not only resulted in faster evolution time, but also achieved better accuracy in emotion recognition.