• Journal of Communication Design
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• v.38
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• pp.80-90
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• 2012

The purpose of this study is to determine the differences in eye-tracking, which is one of the emotional reactions of the autonomic nervous system, against various experimental stimuli that vary in terms of the movement attributes of graphic image. This experiment conducted an analysis of variance of the movement factors(n) in each movement attribute(speed, horizontality movement, verticality movement, diagonal movement). Dependent variables were the fixation starting time, the fixation dwell time, the whole fixation time from stimulus appearance, and the eye-tracking length. The result of eye-tracking against movement speed showed nonsignificant differences for each movement attribute. In the horizontality movement, the → movement was higher than the ← movement. In the verticality movement, the ↑ movement was higher than the ↓ movement. In the diagonal movement, there was not significant differences for each movement attributes.

• Architectural research
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• v.22 no.4
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• pp.155-162
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• 2020

Numerous scholars have explored the modeling, control, and optimization of energy systems in buildings, offering new insights about technology and environments that can advance industry innovation. Eye trackers deliver objective eye-gaze data about visual and attentional processes. Due to its flexibility, accuracy, and efficiency in research, eye tracking has a control scheme that makes measuring rapid eye movement in three-dimensional space possible (e.g., virtual reality, augmented reality). Because eye movement is an effective modality for digital interaction with a virtual environment, tracking how users scan a visual field and fix on various digital objects can help designers optimize building environments and materials. Although several scholars have conducted Virtual Reality studies in three-dimensional space, scholars have not agreed on a consistent way to analyze eye tracking data. We conducted eye tracking experiments using objects in three-dimensional space to find an objective way to process quantitative visual data. By applying a 12 × 12 grid framework for eye tracking analysis, we investigated how people gazed at objects in a virtual space wearing a headmounted display. The findings provide an empirical base for a standardized protocol for analyzing eye tracking data in the context of virtual environments.

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.391-401
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• 2016

Objective: This study aims to examine domestic literature on eye-tracking in the design area, and to present new eye-tracking application directions. Background: Eye-tracking was introduced in the experimental psychology field for the first time in the 1950s. Eye-tracking has high utilization values in the design application area because eye-tracking can accumulate data on what people see, providing the quantitative values on eye movement. Method: This study examined the papers published in domestic journals, as well as the papers presented in conferences from 2000 to 2016 through DBPIA. Results: Although the use of eye-tracking technology was slightly meager in the product design area, it has been actively used for the evaluation analyses of preference and attention in architecture/public design. Eye-tracking also presented a method to design advertisement that is helpful to advertisement effect measurement, and product salesin the advertisement design area. Since detail psychological analysis is possible, the application of eye-tracking in the studies related with user interface has been active. Conclusion: The eye-tracking technology is projected to be actively used as a new interface means, such as in helping in disabled people's communication and in device control, in addition to conventional application areas. Application: This study would be of help to find future research areas of eye-tracking.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.234-239
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• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process fur each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• Sleep Medicine and Psychophysiology
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• v.3 no.1
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• pp.3-14
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• 1996

Eye movement dysfunction has been found in large numbers of schizophrenia patients and their first-degree relatives and can be studied without the interference of deficits in attention, motivation, clinical status and medication effects with relatively easy method. Eye movement dysfunction has been proposed as a useful way of expanding the schizophrenia phenotype in genetic studies. I review the literature on eye movement dysfunction with respect to syndrome and familial specificity and the quantitative assessment of eye tracking. I hope that the etiology and the pathophysiology of schizophrenia can be clarified through this eye movement study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.61-69
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• 2009

In this paper, to propose a new approach to real-time eye tracking. Existing methods of tracking the user's attention to the little I move my head was not going to get bad results for each of the users needed to perform the calibration process. Infrared eye tracking methods proposed lighting and Generalized Regression Neural Networks (GRNN) By using the calibration process, the movement of the head is large, even without the reliable and accurate eye tracking, mapping function was to enable each of the calibration process by the generalization can be omitted, did not participate in the study eye other users tracking was possible. Experimental results of facial movements that an average 90% of cases, other users on average 85% of the eye tracking results were shown.

• Fashion & Textile Research Journal
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• v.20 no.2
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• pp.167-176
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• 2018

In this study, an eye-tracking program that can confirm a design cognition process was developed for the purpose of presenting strategic methods to create fashion images, and the program was used to identify what effects fashion models' external characteristics have on the cognition of design. The data for analysis were collected through an eyemovement tracking experiment and a survey, with the focus on the research problem that differences in models' external uniformity will lead to differences in the eye movement for perceiving models and design as well as the image sensibility. The results of the analysis are as follows. First, it was confirmed that the uniformity of model types and the simplicity/complexity of design led to differences in the eye movement directed at design and models and the gaze ratio. Consequently, it is deemed that models should be selected in consideration of the characteristics of design and the intention of planning when creating fashion images. Second, it was found that in terms of the cognition of design, external conditions of models affect design sensibility. A change in models led to a subtle difference in sensibility cognition even when the design condition did not change. Thus, not only the design but also model attributes are factors that should be considered important in fashion planning.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.20-27
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• 2021

Eye tracking is one of the NUI technologies, and it finds out where the user is gazing. This technology allows users to input text or control GUI, and further analyzes the user's gaze so that it can be applied to commercial advertisements. In the eye tracking system, the allowable range varies depending on the quality of the image and the degree of freedom of movement of the user. Therefore, there is a need for a method of estimating the accuracy of eye tracking in advance. The accuracy of eye tracking is greatly affected by how the eye tracking algorithm is implemented in addition to hardware variables. Accordingly, in this paper, we propose a method to estimate how many degrees of gaze changes when the pupil center moves by one pixel by estimating the maximum possible movement distance of the pupil center in the image.