Eye-Tracking Analysis for Emotion Recognition

Tarnowski, Pawel; Kołodziej, Marcin; Majkowski, Andrzej; Rak, Remigiusz J.

doi:10.1155/2020/2909267

Cited by 49 publications

(37 citation statements)

References 47 publications

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“…However, the highest accuracy received lies below 58% for [184] and 54% for [185] respectively. In [186], a classification between the emotional arousal and valence is attempted while exploiting various eye features correlated with fixations, saccades as well as pupil size resulting to the relatively high accuracy of 80.00% taking into account the three class classification problem. The other two studies concern the binary depressed/non-depressed classification problem.…”

Section: A Emotional Arousal Recognitionmentioning

confidence: 99%

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Skaramagkas

Giannakakis

Ktistakis

et al. 2023

IEEE Rev. Biomed. Eng.

155

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Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eyetracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

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Section: A Emotional Arousal Recognitionmentioning

confidence: 99%

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Skaramagkas

Giannakakis

Ktistakis

et al. 2023

IEEE Rev. Biomed. Eng.

155

View full text Add to dashboard Cite

show abstract

“…Prior work indicated that PD changes can be used as an indicator of arousal states [59], but also are largely affected by the lighting conditions [60]. Recently, Pfleging et al [61] and Tarnowski et al [62] modelled PD as the sum of two contributing factors: (1) PD given lighting conditions, (2) PD given experiences from task. In our study, since 360 • videos were played around and near to the eyes, there was no light source except for the presentation of 360 • videos.…”

Section: Hm and Em Data Analysismentioning

confidence: 99%

“…PD p,average is the average PD of both eyes recorded participant p, while PD is the PD given luminance condition of video v. Following Tarnowski et al's work [62], we used linear regression method (coefficients k, b) to model the relationship between PD and luminance of video v for participant p:…”

Section: Pd Data Analysismentioning

confidence: 99%

CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360$^\circ$ VR Videos

Xue

Ali

Zhang

et al. 2023

IEEE Trans. Multimedia

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Watching 360 • videos using Virtual Reality (VR) head-mounted displays (HMDs) provides interactive and immersive experiences, where videos can evoke different emotions. Existing emotion self-report techniques within VR however are either retrospective or interrupt the immersive experience. To address this, we introduce the Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 • Videos (CEAP-360VR). We conducted a controlled study (N=32) where participants used a Vive Pro Eye HMD to watch eight validated affective 360 • video clips, and annotated their valence and arousal (V-A) continuously. We collected (a) behavioral (head and eye movements; pupillometry) signals ( b) physiological (heart rate, skin temperature, electrodermal activity) responses (c) momentary emotion self-reports (d) within-VR discrete emotion ratings (e) motion sickness, presence, and workload. We show the consistency of continuous annotation trajectories and verify their mean V-A annotations. We find high consistency between viewed 360 • video regions across subjects, with higher consistency for eye than head movements. We furthermore run baseline classification experiments, where Random Forest classifiers with 2s segments show good accuracies for subject-independent models: 66.80% (V) and 64.26% (A) for binary classification; 49.92% (V) and 52.20% (A) for 3-class classification. Our open dataset allows further experiments with continuous emotion self-reports collected in 360 • VR environments, which can enable automatic assessment of immersive Quality of Experience (QoE) and momentary affective states.

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“…Liu, and D. Zhang (2018) and S. Koelstra and I. Patras (2013) based on the results of EEG signals and facial expressions. Tarnowski et al (2020) report obtaining 80 % accuracy when recognizing three classes of emotions (high arousal and high valence; high arousal and low valence; low arousal and moderate valence) with eye-tracking tools. As a result, we may conclude that such techniques can be beneficially used in practice.…”

Section: • Context Formation and Consumer Education Before Visitsmentioning

confidence: 99%

Neuromarketing Research on Individual Responses to Artwork: Implications for Galleries

Чала

Pichyk

Voropai

2021

spne

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This paper reports the results of neuromarketing research for artworks. It was conducted among young people, students of non-art specialties, for nine different widely known art pieces with the help of eyetracking and emotional scanning equipment.The aim of the experiment is to determine (1) whether viewers concentrate more on the representational art compared to abstracts art, (2) whether any correlation between the viewing time and the evoked emotions exists, and (3) whether there is any difference between the monitored emotions and those people articulate (in other words, to what extend artwork viewers tell the truth).The result demonstrates a slightly higher preference in looking at the representational paintings. But we do not notice any correlation between the subjective liking of the painting and the time spent looking at it. An essential difference is recorded for the emotions fixed technically and those reported by the viewers.The paper also suggests several implications for gallery marketing. JEL сlassification: M31, Z11

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Eye-Tracking Analysis for Emotion Recognition

Cited by 49 publications

References 47 publications

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360$^\circ$ VR Videos

Neuromarketing Research on Individual Responses to Artwork: Implications for Galleries

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