Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges
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 spe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/8/2384 |
| _version_ | 1797569911810162688 |
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| author | Jia Zheng Lim James Mountstephens Jason Teo |
| author_facet | Jia Zheng Lim James Mountstephens Jason Teo |
| author_sort | Jia Zheng Lim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:18:01Z |
| format | Article |
| id | doaj.art-122feea66ffc4b6ea8c5898936780bbe |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:18:01Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-122feea66ffc4b6ea8c5898936780bbe2023-11-19T22:21:50ZengMDPI AGSensors1424-82202020-04-01208238410.3390/s20082384Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current ChallengesJia Zheng Lim0James Mountstephens1Jason Teo2Evolutionary Computing Laboratory, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, MalaysiaThe 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.https://www.mdpi.com/1424-8220/20/8/2384affective computingemotion recognitioneye-trackingmachine learningemotion engineering |
| spellingShingle | Jia Zheng Lim James Mountstephens Jason Teo Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges Sensors affective computing emotion recognition eye-tracking machine learning emotion engineering |
| title | Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges |
| title_full | Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges |
| title_fullStr | Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges |
| title_full_unstemmed | Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges |
| title_short | Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges |
| title_sort | emotion recognition using eye tracking taxonomy review and current challenges |
| topic | affective computing emotion recognition eye-tracking machine learning emotion engineering |
| url | https://www.mdpi.com/1424-8220/20/8/2384 |
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