Unobtrusive and pervasive video-based eye-gaze tracking

Cristina, Stefania; Camilleri, Kenneth P.

doi:10.1016/j.imavis.2018.04.002

Cited by 22 publications

(10 citation statements)

References 113 publications

(496 reference statements)

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“…Video-based eye-gaze tracking is at the cutting edge of passive eye tracking techniques. It captures the eye movements non-intrusively utilizing a video camera alone [42]. Video-based eye tracking systems that use pupil and corneal reflection have been developed on the advances on hardware and software.…”

Section: A Video-based Eye Trackingmentioning

confidence: 99%

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Skaramagkas

Giannakakis

Ktistakis

et al. 2023

IEEE Rev. Biomed. Eng.

151

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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 Video-based Eye Trackingmentioning

confidence: 99%

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Skaramagkas

Giannakakis

Ktistakis

et al. 2023

IEEE Rev. Biomed. Eng.

151

View full text Add to dashboard Cite

show abstract

“…Once their face is in the correct area, they have the correct posture, and environmental factors are deemed appropriate, the system determines parameter values that position the gaze on the monitor screen, permitting information from the image domain to be converted to the gaze domain (Cristina & Camilleri, 2018). In the case of EyeSee, based on the nine calibration points displayed on the screen, mapping translates the iris and gaze angles of the participant to the screen coordinates (see Online Appendix A2, Figure 1).…”

Section: Participants Stimuli Campaigns and Proceduresmentioning

confidence: 99%

“…While we would welcome more research in this area, Semmelmann and Weigelt (2018) provide reassurance regarding the applicability of this technology, given the design of our study. It is also clear that the use of webcam-based eye tracking tools is not yet advisable in studies that require high gaze resolution (i.e., dissection of singular items in a crowded display), or focus on very sensitive information (high spatio-temporal resolution, e.g., rapid and continuous recognition of faces embedded in pictures with complex scenes) (Cristina & Camilleri, 2018;Semmelmann & Weigelt, 2018).…”

Section: Limitations and Additional Avenues For Future Researchmentioning

confidence: 99%

Equity Crowdfunders’ Human Capital and Signal Set Formation: Evidence From Eye Tracking

Butticè

Collewaert

Stroe

et al. 2021

Entrepreneurship Theory and Practice

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Signaling theory typically assumes that attention is always given to observable signals. We study signal receivers’ formation of signal sets—the signals to which receivers attend and that they can use for subsequent interpretations. Drawing on a cognitive perspective, we argue that signal receivers’ human capital influences the volume and type of signals they attend to and the time they take to form signal sets. Using eye tracking, we show that equity crowdfunders do not attend to many signals that are easily observable on a campaign page, and that differences in crowdfunders’ human capital uniquely affect their signal set formation.

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“…However, in the appearance-based method, a large number of training samples are often required, and training data under certain conditions are usually adopted. The collected training samples are often dependent on certain persons, so the impact caused by appearance changes cannot be well compensated, and the localization performance is also accidental [6]. 2) In the feature-based method, the specific local features of the eye region are mainly identified to locate the iris center.…”

Section: B Iris Center Localization Methodsmentioning

confidence: 99%

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Dai

Liu

et al. 2020

IEEE Access

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Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications. INDEX TERMS Iris center localization, image gradient, image inpaint, energy map synthesis, post-processing correction.

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Unobtrusive and pervasive video-based eye-gaze tracking

Cited by 22 publications

References 113 publications

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Equity Crowdfunders’ Human Capital and Signal Set Formation: Evidence From Eye Tracking

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

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