• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.548-559
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• 2009

The cascade face detector learned by Adaboost algorithm, which was proposed by Viola and Jones, is state of the art face detector due to its great speed and accuracy. In spite of its great performance, it still suffers from false alarms, and more computation is required to reduce them. In this paper, we want to reduce false alarms with less computation using facial color. Using facial color information, proposed face detection model scans sub-window efficiently and adapts a fast face/non-face classifier at the first stage of cascade face detector. This makes face detection faster and reduces false alarms. For facial color filtering, we define a facial color membership function, and facial color filtering image is obtained using that. An integral image is calculated from facial color filtering image. Using this integral image, its density of subwindow could be obtained very fast. The proposed scanning method skips over sub-windows that do not contain possible faces based on this density. And the face/non-face classifier at the first stage of cascade detector rejects a non-face quickly. By experiment, we show that the proposed face detection model reduces false alarms and is faster than the original cascade face detector.

• Journal of Internet Computing and Services
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• v.3 no.6
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• pp.13-24
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• 2002

This paper presents an automatic approach to detect face and facial feature from face images based on the color information and deformable model. Skin color information has been widely used for face and facial feature diction since it is effective for object recognition and has less computational burden, In this paper, we propose how to compensates varying light condition and utilize the transformed YCbCr color model to detect candidates region of face and facial feature from color images, Moreover, the detected face facial feature areas are subsequently assigned to a initial condition of active contour model to extract optimal boundaries of face and facial feature by resolving initial boundary problem when the active contour is used, The experimental results show the efficiency of the proposed method, The face and facial feature information will be used for face recognition and facial feature descriptor.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.579-582
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• 1999

In this paper, we propose a human face detection algorithm using adaptive skin color model and neural networks. To attain robustness in the changes of illumination and variability of human skin color, we perform a color segmentation of input image by thresholding adaptively in modified hue-saturation color space (TSV). In order to distinguish faces from other segmented objects, we calculate invariant moments for each face candidate and use the multilayer perceptron neural network of backpropagation algorithm. The simulation results show superior performance for a variety of poses and relatively complex backgrounds, when compared to other existing algorithm.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.8
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• pp.1287-1296
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• 1997

The purpose of the study is to make experimental whether three-dimensional body (especially face) has illusion based on theoretical background of form dimensions and color bright among precedent multi-form illusion, using the function of computer simulation. To investigate illusion that factors of clothing design(line, color, material) effect face, as the following is tried to solve giving change to neckline, collar, scarf which is believed to influence near face. How to make experiment as follows watching in order 13 scenes of a pair of with basic design and experiment design. Then the data were subjected to analysis of variance and Duncan's multiple range test. The result of this studying as follows, 1. Face looks larger in complex neckline than simple neckline. The larger collar is the larger face looks. 2. In white jacket, illusion(the lower luminosity of scarf color gets, the brighter face brightness gets) is shown. In black jacket, also illusion(the higher luminosity of scarf color gets, the darker face brightness gets) is shown. 3. In experiment on hardness and softness of face impression according to the material of collar, collar of knit and fur gives us assimilation illusion bring softer impression of face.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1012-1020
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• 2000

In this paper, This paper presents a new algorithm which is used for detecting and extracting human masks from a color still image. The regions where each pixel has a value of skin-color were extracted from the Cb and Cr images, after the tone of the color image is converted to YCbCr from. A morphological filter is used to eliminate noise in the resulting image. By scanning it in horizontal and vertical ways under ways under threshold value, first candidate section is chosen. If it is not a face, secondary candidate section is taken and is divided into two candidate sections. The proposed algorithm is not affected by the variation of illuminations, because it uses only Cb and Cr components in YCbCr color format. Moreover, the face recognition was possible regardless of the degree of shifting face, changed shape, various sizes of the face, and the quality of image.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.559-561
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• 2003

Human face detection plays an important role in variable applications. A face detection method based on skin-color information and facial feature in color images is proposed in this paper. First, the RGB color space is transformed to YCbCr space and only the skin region is extracted with the skin color information. And then, the candidate where face is likely to exist is selected after labeling processing. Finally, we detect facial features in face candidate. The experimental results show that the method proposed here is effective.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.167-174
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• 2013

TIn this paper, we find the face in color images and the ability to track the face was implemented. Face tracking is the work to find face regions in the image using the functions of the computer system and this function is a necessary for the robot. But such as extracting skin color in the image face tracking can not be performed. Because face in image varies according to the condition such as light conditions, facial expressions condition. In this paper, we use the skin color pixel extraction function added lighting compensation function and the entire processing system was implemented, include performing finding the features of eyes, nose, mouth are confirmed as face. Lighting compensation function is a adjusted sine function and although the result is not suitable for human vision, the function showed about 4% improvement. Face features are detected by amplifying, reducing the value and make a comparison between the represented image. The eye and nose position, lips are detected. Face tracking efficiency was good.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1689-1694
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• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.521-524
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• 2003

In this paper, we propose a face region extraction algorithm using color information and projection. After the extraction of face candidate image using adaptive color information, we project it into vertical direction to estimate the width of the face. Then the redundant parts of the face are efficiently removed by using the estimated face width. And the width information of the face is used at the horizontal projection step to extract the height of the face, and non-face region such as the neck and some background regions, which are represented as the similar skin color, effectively eliminated. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.977-984
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• 2007

A realtime face tracking algorithm using motion analysis from image sequences and color information is proposed. Motion area from the realtime moving images is detected by calculating temporal derivatives first, candidate pixels which represent face region is extracted by the fusion filtering with multiple color models, and realtime face tracking is performed by discriminating face components which includes eyes and lips. We improve the stability of face tracking performance by using template matching with face region in an image sequence and the reference template of face components.