Abstract
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.
Highlights
With the development of advanced and affordable wearable sensor technologies, investigations into emotion recognition have become increasingly popular among affective computing researchers since emotion recognition can contribute many useful applications in the fields of neuromarketing, entertainment, computer gaming, health, psychology, and education, among others
The outcomes from emotion recognition studies could sometimes not be entirely accurate since by using images and video clips that are presented by sitting in front of the computer display, such a setup cannot guarantee that the test subject is and exactly focusing on the images or the stimulus
Eye-tracking in the form of gaze concentration has been studied for meditation purposes [106], and the further research of how the performance of such a system could be improved through augmentation of emotion recognition would be highly beneficial since the ability to engage in meditative states has become popular in modern society
Summary
With the development of advanced and affordable wearable sensor technologies, investigations into emotion recognition have become increasingly popular among affective computing researchers since emotion recognition can contribute many useful applications in the fields of neuromarketing, entertainment, computer gaming, health, psychology, and education, among others. Eye movement signals allow us to pinpoint what is attracting the user’s attention and observe their subconscious behaviors They can be important cues for context-aware environments, which contain complementary information for emotion recognition. There are only a very limited number of studies that have developed effective features of eye movements for emotion recognition far In this survey paper, we review the studies and works that present the methods for recognizing emotions based on eye-tracking data. These features include pupil diameter, EOG signals, pupil position, fixation duration, the distance between sclera and iris, motion speed of the eye, and pupillary responses.
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