Facial emotion recognition with a reduced feature set for video game and metaverse avatars

• Published in: Computer animation and virtual worlds Vol. 35; no. 2
• Main Authors: Bellenger, Darren, Chen, Minsi, Xu, Zhijie
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.03.2024
• Subjects: Algorithms; Avatars; Computer & video games; Data transmission; Datasets; Emotion recognition; Emotions; Feature extraction; metaverse; online games; virtual reality
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.2230

This paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. The algorithm aims to reduce data transmission and storage requirements, hurdles in the adoption of emotion recognition in these mediums. The early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research. This paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. Early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research.