Thelxino\"e: Recognizing Human Emotions Using Pupillometry and Machine Learning

arXiv:2403.19014v1 Announce Type: new
Abstract: In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry. We analyze pupil diameter responses to both visual and auditory stimuli via a VR headset and focus on extracting key features in the time-domain, frequency-domain, and time-frequency domain from VR generated data. Our approach utilizes feature selection to identify the most impactful features using Maximum Relevance Minimum Redundancy (mRMR). By applying a Gradient Boosting model, an ensemble learning technique …

abstract analyze arxiv cs.hc cs.lg domain emotion emotions features focus headset human human emotions key machine machine learning reality recognition responses study type via virtual virtual reality visual