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Facial emotion recognition with a reduced feature set for video game and metaverse avatars
  • Darren Bellenger,
  • Minsi Chen,
  • Zhijie Xu
Darren Bellenger
University of Huddersfield

Corresponding Author:[email protected]

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Minsi Chen
University of Huddersfield
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Zhijie Xu
University of Huddersfield
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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.
21 Feb 2023Submitted to Computer Animation and Virtual Worlds
25 Feb 2023Submission Checks Completed
25 Feb 2023Assigned to Editor
10 Mar 2023Reviewer(s) Assigned
09 May 2023Review(s) Completed, Editorial Evaluation Pending
18 Jun 2023Editorial Decision: Revise Major
24 Aug 20231st Revision Received
25 Aug 2023Submission Checks Completed
25 Aug 2023Assigned to Editor
08 Sep 2023Reviewer(s) Assigned
24 Oct 2023Review(s) Completed, Editorial Evaluation Pending
10 Nov 2023Editorial Decision: Revise Minor
20 Nov 20232nd Revision Received