Multiclass emotion classification using pupil size in VR: Tuning support vector machines to improve performance

Lim Jia Zheng and James Mountstephens and Jason Teo Tze Wi (2020) Multiclass emotion classification using pupil size in VR: Tuning support vector machines to improve performance. In: The 2nd Joint International Conference on Emerging Computing Technology and Sports (JICETS), 12-16 Oktober 2025, Hilton Chicago.

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Abstract

Emotion recognition and classification have become a popular topic of research among the area of computer science. In this paper, we present on the emotion classification approach using eye-tracking data solely with machine learning in Virtual Reality (VR). The emotions were classified into four distinct classes according to the Circumplex Model of Affects. The emotional stimuli used for this experiment is 3600 videos presented in VR with four sessions stimulation according to the respective quadrant of emotions. Eye-tracking data is recorded using an eye-tracker and pupil diameter was chosen as a single modality feature for this investigation. The classifier used in this experiment was Support Vector Machine (SVM). The best accuracy is obtained from tuning the parameter in SVM and the best accuracy achieved was 57.65%

Item Type:	Conference or Workshop Item (Other)
Keyword:	Vector Machines, computer science, eye-tracker, pupil diameter
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	JUNAINE JASNI -
Date Deposited:	23 Sep 2025 10:19
Last Modified:	23 Sep 2025 10:19
URI:	https://eprints.ums.edu.my/id/eprint/45160

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