Research on emotion cognitive classification based on eye movement in virtual reality

Abstract

With the rise of virtual reality technology and eye movement tracking, the cognitive classification of eye movement emotion combined with virtual reality technology was a research hotspot recently. In this research, 96 pictures were selected from the Chinese Affective Picture System (CAPS), and 35 subjects were stimulated with pictures in virtual scene. The corresponding eye movement data were tracked synchronously. Eye movement data were progressed with blink data reconstruction and baseline correction, after which time and frequency domain features were extracted. Finally, for three emotion model dimensions including valence, arousal and dominance expressed as high, medium and low, models are trained with Support Vector Machine (SVM), with the highest classification accuracy of single person reaches 78.1%. The results of this paper are instructive for the processing of eye movement data, and the eye tracking data combined with virtual reality technology can be used for emotion recognition research through the proposed preprocessing methods and feature extraction.