• 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 Digital Convergence
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• v.11 no.2
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• pp.215-220
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• 2013

They are well-known geometric properties of a circle that the perpendicular bisector of a chord passes through the center of a circle, and the intersection of the perpendicular bisectors of any two chords is its center. This paper is related to a fast pupil detection method capable of detecting the center and the radius of a pupil using these geometric properties at high speed when detecting the pupil region for iris segmentation. The proposed method is characterized as rapidly detecting the center and the radius of the pupil, extracting the candidate points of the circle in human eye images using morphological operations, and finding two chords using four points on the circular edge, and taking the intersection of the perpendicular bisectors of these two chords for its center. The proposed method can not only detect the center and the radius of a pupil rapidly but also find partially occluded pupils in human eye images.

• Journal of Broadcast Engineering
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• v.19 no.4
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• pp.478-490
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• 2014

In order to make 3-D display technique a better tool to provide viewers with realistic stereoscopic experience, various researches have been done in the many relevant fields. This psychophysical study was designed to investigate whether there was any difference in the perceptual processing between a dominant and non-dominant eye when a 3-D cue was provided exclusively to only one eye. We measured the reaction time for detecting a depth change by providing the viewer's each eyes with differential 3-D stimuli, which have systematical patterns. We obtained that there was a consistent 3-D perceptual performance when the 3-D cue was provided to the viewers' left eye regardless of their eye dominance. The result suggests that it might be a better technique to arrange the camera for left eye to carry 3-D cues to get the viewer's consistent 3-D perception.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.7
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• pp.544-550
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• 2000

Among the several types of 3D display methods the autostereoscopic method has an advantage that we can enjoy a 3D image without any additional device but the method has a disadvantage of a narrow viewing zone so that the moving viewer coannot see the 3D image continuously. This disadvantage can be overcome with the detectioni of viewer's positional movement by head tracking. In this paper we suggest a method of detecting the position of the moving viewer's two eyes by using images obtained through a color CCD camera, The suggested method consists of the preprocessing process and the eye-detection process. Through the experiment of applying the suggested method we were able to find the accurate two-eyes position for 78 images among 80 sample input images of 8 different men with the processing speed of 0.39 second/frame using a personal computer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.714-720
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• 2004

In detecting human faces in color images, additional geometric computation is often necessary for validating the face-candidate regions having various forms. In this paper, we propose a method that detects the facial features using chrominance components of color which do not affected by face occlusion and orientation. The proposed algorithm uses the property that the Cb and Cr components have consistent differences around the facial features, especially eye-area. We designed the Mean-Gray Morphology operator to emphasize the feature areas in the eye-map image which generated by basic chrominance differences. Experimental results show that this method can detect the facial features under various face candidate regions effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1429-1436
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• 2009

In this paper, we propose a robust eye detection method using template matching based on genetic algorithm in the single facial image. The previous works for detecting pupil using genetic algorithm had a problem that the detection accuracy is influnced much by the initial population for it's random value. Therefore, their detection result is not consistent. In order to overcome this point we extract local minima in the facial image and generate initial populations using ones that have high fitness with a template. Each chromosome consists of geometrical informations for the template image. Eye position is detected by template matching. Experiment results verify that the proposed eye detection method improve the precision rate and high accuracy in the single facial image.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.1-8
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• 2018

In the field of biometric security such as face and iris recognition, it is essential to extract facial features such as eyes and lips. In this paper, we have studied a method of detecting eye and lip region in face image using faster R-CNN. The faster R-CNN is an object detection method using deep running and is well known to have superior performance compared to the conventional feature-based method. In this paper, feature maps are extracted by applying convolution, linear rectification process, and max pooling process to facial images in order. The RPN(region proposal network) is learned using the feature map to detect the region proposal. Then, eye and lip detector are learned by using the region proposal and feature map. In order to examine the performance of the proposed method, we experimented with 800 face images of Korean men and women. We used 480 images for the learning phase and 320 images for the test one. Computer simulation showed that the average precision of eye and lip region detection for 50 epoch cases is 97.7% and 91.0%, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.83-86
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• 2003

This paper suggests user's interface with computer by means of detecting gaze under HMD, head mounted display, environment. System is derived as follows; firstly, calibrate a camera in HMD, which determines geometrical relationship between monitor and captured image. Second, detect the center of pupil using algorithm of the center of mass and represent a gazing position on the computer screen. If user blinks or stares at a certain position for a while, message is sent to computer. Experimental results show the center of mass is robust against glint effects, and detecting error was 7.1%. and 4.85% in vertical and horizontal direction, respectively. To adjust detailed movement of a mouse takes 0.8 sec more. The 98% of blinking is detected successfully and 94% of clicking detection is resulted.