• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.828-831
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• 2005

In this paper we propose a method to detect human faces from color image using pupil-template matching. Face detection is done by three stages. (i)separating skin regions from non-skin regions; (ii)generating a face regions by application of the best-fit ellipse; (iii)detecting face by pupil-template. Detecting skin regions is based on a skin color model. we generate a gray scale image from original image by the skin model. The gray scale image is segmented to separated skin regions from non-skin regions. Face region is generated by application of the best-fit ellipse is computed on the base of moments. Generated face regions are matched by pupil-template. And we detection face.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1332-1341
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• 1999

본 논문에서는 시간에 따라 위치 및 회전각도가 변하는 얼굴 영상을 분석하여 눈과 입을 추출하는 방법을 제안한다. 동영상에서의 얼굴 영역을 효과적으로 추적하기 위해 간편화된 칼만 필터를 제안하며, 예측된 얼굴 영역 내에서 얼굴의 회전 각도를 고려하여 수직 및 수평 프로파일을 적용함으로써 좀 더 정교하게 얼굴 구성요소를 추출한다. 제안한 방법의 효율성은 실험 결과를 통하여 보인다.Abstract We propose the method that extracts eyes and mouth of human by analysing facial image sequences which can change their positions and orientations along the time. We propose the simplified Kalman filter to track the area of human face efficiently in image sequences. We also devise the method of slant-compensation, so that the facial components could be extracted more accurately by using vertical and horizontal profiles of the compensated images. Finally, we show the effectiveness of the suggested method through experimental results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.124-127
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• 1997

In this paper, we propose the new face detection and tracking angorithm in sequential images which have complex background. In order to apply face deteciton algorithm efficently, we convert the conventional RGB coordiantes into CIE coordonates and make the input images insensitive to luminace. And human face shapes and colors are learned using ueural network's backpropagation. For variable face size, we make mosaic size of input images vary and get the face location with various size through neural network. Besides, in sequential images, we suggest face motion tracking algorithm through image substraction processing and thresholding. At this time, for accurate face tracking, we use the face location of previous. image. Finally, we verify the real-time applicability of the proposed algorithm by the simple simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.63-71
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• 1996

This paper proposes a method which analyzes facial shape and extracts positions of eyes regardless of the tilt and the size of input iamge. With the extracted feature parameters of facial element by the method, full human faces are recognized by a neural network which BP algorithm is applied on. Input image is changed into binary codes, and then labelled. Area, circumference, and circular degree of the labelled binary image are obtained by using chain code and defined as feature parameters of face image. We first extract two eyes from the similarity and distance of feature parameter of each facial element, and then input face image is corrected by standardizing on two extracted eyes. After a mask is genrated line historgram is applied to finding the feature points of facial elements. Distances and angles between the feature points are used as parameters to recognize full face. To show the validity learning algorithm. We confirmed that the proposed algorithm shows 100% recognition rate on both learned and non-learned data for 20 persons.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.42-45
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• 2010

This paper proposes an intelligent video surveillance system for human object tracking. The proposed system integrates the object extraction, human object recognition, face detection, and camera control. First, the object in the video signals is extracted using the background subtraction. Then, the object region is examined whether it is human or not. For this recognition, the region-based shape descriptor, angular radial transform (ART) in MPEG-7, is used to learn and train the shapes of human bodies. When it is decided that the object is human or something to be investigated, the face region is detected. Finally, the face or object region is tracked in the video, and the pan/tilt/zoom (PTZ) controllable camera tracks the moving object with the motion information of the object. This paper performs the simulation with the real CCTV cameras and their communication protocol. According to the experiments, the proposed system is able to track the moving object(human) automatically not only in the image domain but also in the real 3-D space. The proposed system reduces the human supervisors and improves the surveillance efficiency with the computer vision techniques.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1258-1268
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• 2004

We propose a new fast, algorithm which is used for detecting frontal face in the frequency domain based on human skin-color using OCT coefficient of dynamic image compression and skin color information. The region where each pixel has a value of skin-color were extracted from U and V value based on DCT coefficient obtained in the process of Image compression using skin-color map in the Y, U, V color space A morphological filter and labeling method are used to eliminate noise in the resulting image We propose the algorithm to detect fastly human face that estimate the directional feature and variance of luminance block of human skin-color Then Extraction of face was completed adaptively on both background have the object analogous to skin-color and background is simple in the proposed algorithm The performance of face detection algorithm is illustrated by some simulation results earned out on various races We confined that a success rate of 94 % was achieved from the experimental results.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.1
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• pp.20-36
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• 2024

Over the past three years, even amidst viral threats, a notable shift towards online interactions has been observed. This trend persists the presence of significant viral concerns. Our study centered on female office workers in their twenties and thirties in Korea, seeking to comprehend how they enhance and present their external image in the digital era. We explored the use of digital devices and fashion choices that enable them to amplify their self-expression in video conferences. Using a mix of surveys and in-depth interviews, we employed snowball sampling to recruit twelve participants. These women were given the opportunity to shape their digital persona either to uphold their current image or to adapt it for interactions where they weren't face-to-face. Their desired images fell into three distinct categories: an authoritative professional image, a clean modern image, and a natural image. Depending on the context, the participants aimed to convey these images independently or in various combinations. Our findings suggest the need to develop strategies for acknowledging and projecting individual fashion identities in non-face-to-face interactions. Such strategies would empower individuals to better align their online personas with their desired self-image, whether it's professional, modern, clean, natural, or a combination thereof.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.115-118
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• 2001

An image processing method was applied to characterize a shape of the flexible grinding disk. A disk surface image was taken by CCD camera. Depth of cut was changed to be 2 and 4mm. Circles marked on the disk were captured to extract the key features of the deflection. Notable correlation has been observed between the intervals and the process conditions. Same methodology has been applied to check the symmetry of the human face. Tentative results revealed that symmetry could be checked using the filtered face image.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.672-679
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• 2011

In this paper, detection algorithm of face region based on skin color of in the TV images is proposed. In the first, reference image is set to the sampled skin color, and then the extracted of face region is candidated using the Euclidean distance between the pixels of TV image. The eye image is detected by using the mean value and standard deviation of the component forming color difference between Y and C through the conversion of RGB color into CMY color model. Detecting the lips image is calculated by utilizing Q component through the conversion of RGB color model into YIQ color space. The detection of the face region is extracted using basis of knowledge by doing logical calculation of the eye image and lips image. To testify the proposed method, some experiments are performed using front color image down loaded from TV color image. Experimental results showed that face region can be detected in both case of the irrespective location & size of the human face.