Emotion detection is a fundamental component in the field of Affective Computing. Proper recognition of emotions can be useful in improving the interaction between humans and machines, for instance, with regard to designing effective user interfaces. This study aims to understand the relationship between emotion and pupil dilation. The Tobii Pro X3-120 eye tracker was used to collect pupillary responses from 30 participants exposed to content designed to evoke specific emotions. Six different video scenarios were selected and presented to participants, whose pupillary responses were measured while watching the material. In total, 16 data features (8 features per eye) were extracted from the pupillary response distribution during content exposure. Through logistical regression, a maximum of 76% classification accuracy was obtained through the measurement of pupillary response in predicting emotions classified as fear, anger, or surprise. Further research is required to precisely calculate pupil size variations in relation to emotionally evocative input in affective computing applications.
Keywords: affective computing; emotional recognition; machine learning; pupillary response.