Emotion Recognition via Agent-Based Modelling

Emotion recognition is one of the most popular research areas in recent times. Emotion recognition is also made from facial expressions and sound signals, as can be done biomedical signals. Especially when face-to face communication is not possible, emotion can he recognized from the sound data. In this study, emotion recognition was performed from the sound data. One of the most important steps in feeling recognition is feature selection. Feature selection can be done in many different ways. In this study, a new agent-based approach to emotion recognition is presented. The agent-based modeling features were then selected by opt-ainet optimization method. The goal is automatic selection of features that give the best classification accuracy.