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Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges
Open accessAbstract
The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Active Shape Models-Their Training and Application
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1004 · Cited by 7,190
PsychoPy—Psychophysics software in Python
Peirce, J. W. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.11.017 · Cited by 4,603
Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697196 · Cited by 4,297
What are emotions? And how can they be measured?
Scherer, K. R. (2005) · Social Science Information · doi:10.1177/0539018405058216 · Cited by 4,264
Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O., Furlan, R., Pizzinelli, P., Sandrone, G., Malfatto, G., Dell'Orto, S., & Piccaluga, E. (1986) · Circulation Research · doi:10.1161/01.res.59.2.178 · Cited by 4,130
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
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Advances in the use of virtual reality to treat mental health conditions
Bell, I. H., Pot-Kolder, R., Rizzo, A., Rus-Calafell, M., Cardi, V., Cella, M., Ward, T., Riches, S., Reinoso, M., Thompson, A., et al. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00334-9 · Cited by 140
State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy
Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11
Metadata
- Authors
- Lim, J. Z., Mountstephens, J., & Teo, J.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-04-22
- Volume
- 20
- Issue
- 8
- Pages
- 2384
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3020487153
- Cited by
- 293
- References
- 88
- FWCI
- 30.30
- Type
- journal article
- Language
- en
- Open access
- gold
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