A conceptual design of a deep learning framework for emotion-driven optimization of virtual reality content

This study introduces a concept of the innovative framework designed to enhance the quality of conventional Virtual Reality (VR) experiences by leveraging the indirect emotion recognition technique for VR participants. Specifically, we concentrate on VR environments capable of mapping a user's facial expression onto a corresponding avatar. The user's emotion is indirectly inferred through the emotive expressions displayed by their avatar. In order to achieve accurate emotion recognition, we employ a Convolutional Neural Network (CNN)-based deep learning algorithm. The algorithm successfully categorizes emotions into one of seven distinct states-anger, disgust, fear, happiness, sadness, surprise, and neutrality-with an accuracy rate of 87%. The results of this emotion analysis are systematically archived and visualized for in-depth evaluation. We expect that the proposed framework could offer VR development companies a robust tool for timeline-based user response analysis, thereby enabling them to refine and optimize their VR content in alignment with user sentiment.