A Novel Technique to Develop Cognitive Models for Ambiguous Image Identification Using Eye Tracker

Roy, Anup Kumar; Akhtar, Md. Nadeem; Mahadevappa, Manjunatha; Guha, Rajlakshmi; Mukherjee, Jayanta

doi:10.1109/taffc.2017.2768026

Cited by 14 publications

(10 citation statements)

References 48 publications

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“…Vidyapu et al (2019) proposed an attention prediction on webpage images using multilabel classification. In a study by Roy et al (2017), the authors developed a method for the cognitive process to identify ambiguous images.…”

Section: Fixationmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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ContextEye tracking is a technology to measure and determine the eye movements and eye positions of an individual. The eye data can be collected and recorded using an eye tracker. Eye-tracking data offer unprecedented insights into human actions and environments, digitizing how people communicate with computers, and providing novel opportunities to conduct passive biometric-based classification such as emotion prediction. The objective of this article is to review what specific machine learning features can be obtained from eye-tracking data for the classification task.MethodsWe performed a systematic literature review (SLR) covering the eye-tracking studies in classification published from 2016 to the present. In the search process, we used four independent electronic databases which were the IEEE Xplore, the ACM Digital Library, and the ScienceDirect repositories as well as the Google Scholar. The selection process was performed by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) search strategy. We followed the processes indicated in the PRISMA to choose the appropriate relevant articles.ResultsOut of the initial 420 articles that were returned from our initial search query, 37 articles were finally identified and used in the qualitative synthesis, which were deemed to be directly relevant to our research question based on our methodology.ConclusionThe features that could be extracted from eye-tracking data included pupil size, saccade, fixations, velocity, blink, pupil position, electrooculogram (EOG), and gaze point. Fixation was the most commonly used feature among the studies found.

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Section: Fixationmentioning

confidence: 99%

Eye-Tracking Feature Extraction for Biometric Machine Learning

2022

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“…Thus, gaze input can support (Fares et al, 2013) or substitute (Templier et al, 2016) manual modes of interaction. According to Roy et al (2017), eye fixations can be used to develop a model to predict the objects that a user is observing, which is a hint to how that user is interpreting a scene. Saccadic eye movements can enable predicting cognitive load, mental fatigue, attention, emotion, anxiety according to work by Duchowski et al (2019).…”

Section: Eye Movementsmentioning

confidence: 99%

Telelife: The Future of Remote Living

Orlosky

Sra

Bektaş

et al. 2021

Front. Virtual Real.

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In recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected almost all aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has exacerbated the fact that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce “Telelife,” a vision for the near and far future that depicts the potential means to improve remote living and better align it with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual/physical rapid prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics and haptic feedback, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, we provide a meta-synthesis in this paper that will guide future research on remote living.

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“…Thus, gaze input can support Fares et al (2013) or substitute Templier et al (2016) manual modes of interaction. According to Roy et al Roy et al (2017), eye fixations can be used to develop a model to predict the objects that a user is observing, which is a hint to how that user is interpreting a scene. Saccadic eye movements can enable predicting cognitive load, mental fatigue, attention, emotion, anxiety according to work by Duchowski et al Duchowski et al (2019).…”

Section: Eye Movementsmentioning

confidence: 99%

Telelife: The Future of Remote Living

Orlosky¹,

Sra²,

Bektaş³

et al. 2021

Preprint

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In recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected various aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has revealed that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce "Telelife," a vision for the near future that depicts the potential means to improve remote living better aligned with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, the directions outlined in this paper will guide future research on remote living.

show abstract

A Novel Technique to Develop Cognitive Models for Ambiguous Image Identification Using Eye Tracker

Cited by 14 publications

References 48 publications

Eye-Tracking Feature Extraction for Biometric Machine Learning

Eye-Tracking Feature Extraction for Biometric Machine Learning

Telelife: The Future of Remote Living

Telelife: The Future of Remote Living

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