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Eye-Tracking Analysis for Emotion Recognition
Joint Authors
Tarnowski, Paweł
Kołodziej, Marcin
Majkowski, Andrzej
Rak, Remigiusz Jan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-01
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This article reports the results of the study related to emotion recognition by using eye-tracking.
Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments.
Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter.
To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies.
Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence.
A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method.
American Psychological Association (APA)
Tarnowski, Paweł& Kołodziej, Marcin& Majkowski, Andrzej& Rak, Remigiusz Jan. 2020. Eye-Tracking Analysis for Emotion Recognition. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138731
Modern Language Association (MLA)
Tarnowski, Paweł…[et al.]. Eye-Tracking Analysis for Emotion Recognition. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1138731
American Medical Association (AMA)
Tarnowski, Paweł& Kołodziej, Marcin& Majkowski, Andrzej& Rak, Remigiusz Jan. Eye-Tracking Analysis for Emotion Recognition. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138731
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138731