Online Emotion Recognition


This project is aimed at building a system to recognize emotional expression given four physiological signals. Data was gathered from a graduate student with acting experience as she intentionally tried to experience eight different emotional states daily over a period of several weeks. Several features are extracted from each of her physiological signals. The first classifiers gave a classification result of 88% success when discriminating among 3 emotions (pure chance would be 33.3%), and of 51% when discriminating among 8 emotions (pure chance 12.5%). New, improved classifiers reach an 81% success rate when discriminating among all 8 emotions. Furthermore, an online classifier has now been built using the old method, which gives a success rate only 8% less than its old offline counterpart (i.e. 43%). We expect this percentage to sharply increase when the new methods are adapted to run online.