AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

Abstract

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies.