• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1162-1170
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• 2014

In this paper, an accurate remote gaze tracking method with two cameras is presented using a modified Starburst algorithm and honography normalization. Starburst algorithm, which was originally developed for head-mounted systems, often fails in detecting accurate pupil centers in remote tracking systems with a larger field of view due to lots of noises. A region of interest area for pupil is found using template matching, and then only within this area Starburst algorithm is applied to yield pupil boundary candidate points. These are used in improved RANSAC ellipse fitting to produce the pupil center. For gaze estimation robust to head movement, an improved homography normalization using four LEDs and calibration based on high order polynomials is proposed. Finally, it is shown that accuracy and robustness of the system is improved using two cameras rather than one camera.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.11-19
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• 2016

The size of a display is large, The form becoming various of that do not apply to previous methods of gaze tracking and if setup gaze-track-camera above display, can solve the problem of size or height of display. However, This method can not use of infrared illumination information of reflected cornea using previous methods. In this paper, Robust pupil detecting method for eye's occlusion, corner point of inner eye and center of pupil, and using the face pose information proposes a method for calculating the simply position of the gaze. In the proposed method, capture the frame for gaze tracking that according to position of person transform camera mode of wide or narrow angle. If detect the face exist in field of view(FOV) in wide mode of camera, transform narrow mode of camera calculating position of face. The frame captured in narrow mode of camera include gaze direction information of person in long distance. The method for calculating the gaze direction consist of face pose estimation and gaze direction calculating step. Face pose estimation is estimated by mapping between feature point of detected face and 3D model. To calculate gaze direction the first, perform ellipse detect using splitting from iris edge information of pupil and if occlusion of pupil, estimate position of pupil with deformable template. Then using center of pupil and corner point of inner eye, face pose information calculate gaze position at display. In the experiment, proposed gaze tracking algorithm in this paper solve the constraints that form of a display, to calculate effectively gaze direction of person in the long distance using single camera, demonstrate in experiments by distance.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.575-580
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• 2010

Eye gaze as a form of input was primarily developed for users who are unable to use usual interaction devices such as keyboard and the mouse; however, with the increasing accuracy in eye gaze detection with decreasing cost of development, it tends to be a practical interaction method for able-bodied users in soon future as well. This paper explores a low-cost, robust, rotation and illumination independent eye gaze system for gaze enhanced user interfaces. We introduce two brand-new algorithms for fast and sub-pixel precise pupil center detection and 2D Eye Gaze estimation by means of deformable template matching methodology. In this paper, we propose a new algorithm based on the deformable angular integral search algorithm based on minimum intensity value to localize eyeball (iris outer boundary) in gray scale eye region images. Basically, it finds the center of the pupil in order to use it in our second proposed algorithm which is about 2D eye gaze tracking. First, we detect the eye regions by means of Intel OpenCV AdaBoost Haar cascade classifiers and assign the approximate size of eyeball depending on the eye region size. Secondly, using DAISMI (Deformable Angular Integral Search by Minimum Intensity) algorithm, pupil center is detected. Then, by using the percentage of black pixels over eyeball circle area, we convert the image into binary (Black and white color) for being used in the next part: DTBGE (Deformable Template based 2D Gaze Estimation) algorithm. Finally, using DTBGE algorithm, initial pupil center coordinates are assigned and DTBGE creates new pupil center coordinates and estimates the final gaze directions and eyeball size. We have performed extensive experiments and achieved very encouraging results. Finally, we discuss the effectiveness of the proposed method through several experimental results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.6
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• pp.539-547
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• 2007

In this paper, development and tests of a real-time eye-tracker system are discussed. The tracker system tracks a user's gaze point through movement of eyes by means of vision-based pupil detection. The vision-based method has an advantage of detecting the exact positions of user's eyes. An infrared camera and a LED are used to acquire a user's pupil image and to extract pupil region, which was hard to extract with software only, from the obtained image, respectively. We develop a pupil-tracking algorithm with Kalman filter and grab the pupil images by using DSP(Digital Signal Processing) system for real-time image processing technique. The real-time eye-tracker system tracks the movements of user's pupils to project their gaze point onto a background image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.167-170
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• 2010

In this paper, we proposed a novel algorithm to detect the center of pupil in frontal view face. This algorithm, at first, extract an eye region from the face image using integral projection function and variance projection function. In an eye region, detect the center of pupil positions using circular hough transform with sobel edge mask. The experimental results show good performance in detecting pupil center from FERET face image.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.327-336
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• 2021

In this paper, we will introduce the image processing methods for the remote pupillary light reflex measurement using the video taken by a general smartphone camera without a special device such as an infrared camera. We propose an algorithm for estimate the size of the pupil that changes with light using image data analysis without a learning process. In addition, we will introduce the results of visualizing the change in the pupil size by removing noise from the recorded data of the pupil size measured for each frame of the video. We expect that this study will contribute to the construction of an objective indicator for remote pupillary light reflex measurement in the situation where non-face-to-face communication has become common due to COVID-19 and the demand for remote diagnosis is increasing.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.250-259
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• 2006

The information of eye movement is used in various fields such as psychology, ophthalmology, physiology, rehabilitation medicine, web design, HMI(human-machine interface), and so on. Various devices to detect the eye movement have been developed but they are too expensive. The general methods of eye movement tracking are EOG(electro-oculograph), Purkinje image tracker, scleral search coil technique, and video-oculograph(VOG). The purpose of this study is to embody the algorithm which tracks the location of the gazing point at a pupil. Two kinds of location data were compared to track the gazing point. One is the reference points(infrared LEDs) which is effected from the globe. Another is the center point of the pupil which is gained with a CCD camera. The reference point was captured with the CCD camera and infrared lights which were not recognized by human eyes. Both of images which were thrown and were not thrown an infrared light on the globe were captured and saved. The reflected reference points were detected with the brightness difference between the two saved images. In conclusion, the circumcenter theory of a triangle was used to look for the center of the pupil. The location of the gazing point was relatively indicated with the each center of the pupil and the reference point.

• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.107-113
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• 2014

In this study, we design and implement an eye-gaze estimation system based on the extraction of eye contour and pupil region. In order to effectively extract the contour of the eye and region of pupil, the face candidate regions were extracted first. For the detection of face, YCbCr value range for normal Asian face color was defined by the pre-study of the Asian face images. The biggest skin color region was defined as a face candidate region and the eye regions were extracted by applying the contour and color feature analysis method to the upper 50% region of the face candidate region. The detected eye region was divided into three segments and the pupil pixels in each pupil segment were counted. The eye-gaze was determined into one of three directions, that is, left, center, and right, by the number of pupil pixels in three segments. In the experiments using 5,616 images of 20 test subjects, the eye-gaze was estimated with about 91 percent accuracy.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1171-1176
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• 2014

The eye-tracking [ET] is used on the human computer interaction [HCI] analysing the movement status as well as finding the gaze direction of the eye by tracking pupil's movement on a human face. Nowadays, the ET is widely used not only in market analysis by taking advantage of pupil tracking, but also in grasping intention, and there have been lots of researches on the ET. Although the vision based ET is known as convenient in application point of view, however, not robust in changing environment such as illumination, geometrical rotation, occlusion and scale changes. This paper proposes two steps in the ET, at first, face and eye regions are discriminated by Haar classifier on the face, and then the pupils from the discriminated eye regions are tracked by CAMShift as well as Template matching. We proved the usefulness of the proposed algorithm by lots of real experiments in changing environment such as illumination as well as rotation and scale changes.