Abstract
Emotion recognition is a burgeoning field in machine learning. Previous approaches have focused on classification of expressions from 2D facial images or voice acoustics. However, this focus on external body signals overlooks the rich source of internal physiological changes that occur within the body during an emotional event. In this work, we developed the Open Access data set PeakAffectDS, which contains physiological recordings that includes external (facial electromyography) and internal (electrocardiogram and respiration) markers of emotion. Fifty-one participants were recorded while viewing evocative movie clips. We applied deep learning methods to classify induced emotion on PeakAffectDS, with accuracy of 0.24, 0.21, 0.18, and 0.18 for six class classification on zygomaticus EMG, corrugator EMG, ECG, and respiration respectively. The modeling results are underwhelming, but preliminary analysis of participant responses are encouraging and validate the study design.