• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.3
• /
• pp.241-246
• /
• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.10
• /
• pp.53-62
• /
• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• The Journal of the Korea Contents Association
• /
• v.21 no.3
• /
• pp.1-9
• /
• 2021

The hologram pyramid is an application of floating holograms, allowing the observer to see three-dimensional holograms from various angles without wearing wearable devices. Due to the low cost and ease of manufacturing, it has been used in a wide variety of fields as diverse as education, prototyping, showcase, and etc. But, when the observer looks at the hologram from the place where each side of the hologram pyramid is connected, the hologram looks cut and distorted. Also, the observer can see the only hologram of angles viewed head-on from each side. In this paper, we propose a method of generating a hologram image corresponding to the observer's gaze angle by tracking the observer's position and conducting reverse distortion. It provide a hologram of the angle viewed by the observer without cutting and distortion. In addition, the existing method and the proposed method were applied and compared in the hologram pyramid.

• Journal of The Korean Association For Science Education
• /
• v.36 no.4
• /
• pp.705-715
• /
• 2016

The purpose of this research is to analyze elementary students' attention characteristics through attention test and eye tracking on real science classes. The SMI's ETG(eye tracker glasses) mobile eye tracker was used to analyze the attention process of elementary students'. The sampling rate of the ETG is 30Hz. The participants of attention test were elementary 155 6th-grade elementary students and the participants for the eye-tracker were six 6th-grade male students. The eye movements were analyzed using the 'BeGaze Mobile Video Analysis Package' program. The results of this research are as follows. First, the attention test results of elementary students showed high correlation between selective attention and sustained attention (.85) and low correlation between selective attention and self-regulation (.32). Second, the attention types of elementary students were divided into four; attention, inattention, easygoing and hasty. Third, elementary students' attention were divided into top-down, bottom-up, default mode network through analysis of elementary students′ eye-movements during real science classes. Also their attention shift occurred frequently due to various reasons in real class situation. There were three reasons that made elementary students fail to handle knowledge-dependent top-down attention; 1) the cognitive failure of target caused by failing to focus attention, 2) the absence of prior knowledge on target object, 3) the analogical failure of prior knowledge. Finally, elementary students' attention process were schematized based on the analysis of students' eye movements and attention test. This research is expected to be utilized as basic data for developing effective teaching strategies, teaching-learning models and instructional materials.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.113-127
• /
• 2012

Face detection methods using image processing have been proposed in many different ways. Generally, the most widely used method for face detection is an Adaboost that is proposed by Viola and Jones. This method uses Haar-like feature for image learning, and the detection performance depends on the learned images. It is well performed to detect face images within a certain distance range, but if the image is far away from the camera, face images become so small that may not detect them with the pre-learned Haar-like feature of the face image. In this paper, we propose the far distance face detection method that combine the Aadaboost of Viola-Jones with a saliency map and user's attention information. Saliency Map is used to select the candidate face images in the input image, face images are finally detected among the candidated regions using the Adaboost with Haar-like feature learned in advance. And the user's eye-tracking information is used to select the interest regions. When a subject is so far away from the camera that it is difficult to detect the face image, we expand the small eye gaze spot region using linear interpolation method and reuse that as input image and can increase the face image detection performance. We confirmed the proposed model has better results than the conventional Adaboost in terms of face image detection performance and computational time.

• Science of Emotion and Sensibility
• /
• v.21 no.2
• /
• pp.15-28
• /
• 2018

The purpose of this study was to conduct eye-tracking experiments to target large spaces and to analyze the characteristics of pupil gaze by gender. By analyzing the change of pupil size in the eye-tracking experiment, we suggest a template for objective and scientific analyses of gender observation data. Additionally, based on the difference between gender and time to pupil size change, we noted the characteristic of time that gather the visual perception data information and showed that females attend to interesting elements of visual information one to two seconds slower than males. In the initial "$1sec{\rightarrow}2sec$", the pupil size has been increased in leap condition of male and in fixed condition of female. In addition, if condition limits to fixed observation to view the change rates on gender then the pupil size of female was larger than male before 9 seconds, while male's pupil size was larger after 9 seconds. This indicates that females obtain visual information through a relatively larger pupil size during the first 1-8 seconds after stimulus presentation, while males acquire it between 10-15 seconds after input. However, based on the result that the pupil size of female was larger than male on the change of observation time, the pupil size movement on female was energized to watch more interest element after a certain period of time.

• Journal of Science Education
• /
• v.37 no.1
• /
• pp.142-156
• /
• 2013

The purpose of this study was to analyze observation characteristics through eye movement according to cognitive style. For this, developed observation task that can be shown the difference between wholistic cognitive style group and analytic cognitive style group, measured eye movement of university students who has different cognitive style, as given observation task. It is confirmed the difference between two cognitive style groups by analysing gathered statistics and visualization data. The findings of this study were as follows; First, Compared observation sequence and pattern by cognitive style, analytic cognitive style group is concerned with spider first and moving on surrounding environment, whereas wholistic cognitive style group had not fixed pattern as observing spider itself and surrounding area of spider alternately or looking closely on particular part at first. When observing entire feature and partial feature, wholistic cognitive style group was moving on Fixation from outstanding factor without fixed pattern, analytic cognitive style had certain directivity and repetitive investigation. Second, compared the ratio of observation, analytic cognitive style group gave a large part to spider the very thing, wholistic cognitive style group gave weight to surrounding area of spider, and analytic group shown higher concentration on observing partial feature, wholistic cognitive style group shown higher concentration on observing wholistic feature. Wholistic cognitive style group gave importance to partial features in surrounding area, and wholistic feature of spider than analytic cognitive style group, analytic cognitive style group was focus on partial features of spider than wholistic cognitive style group. Through the result of this study, there are differences of observing time, frequency, object, area, sequence, pattern and ratio from cognitive styles. It is shown the reason why each student has varied outcome, from the difference of information following their cognitive style, and the result of this study help to figure out and give direction to what observation fulfillment is suitable for each student.