• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.1-10
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• 2013

This paper presents an approach for detecting and measuring human skin pigmentation. In the proposed scheme, we extract a skin area by a Gaussian skin color model that is estimated from the statistical analysis of training images and remove tiny noises through the morphology processing. A skin area is decomposed into two components of hemoglobin and melanin by an independent component analysis (ICA) algorithm. Then, we calculate the intensities of hemoglobin and melanin by using the location histogram and determine the existence of skin pigmentation according to the global and local distribution of two intensities. Furthermore, we measure the area and density of the detected skin pigmentation. Experimental results verified that our scheme can both detect the skin pigmentation and measure the quantity of that and also our scheme takes less time because of the location histogram.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.329-330
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• 2007

This paper presents a simple and robust face detection algorithm that can be utilized to video summary. We firstly apply the Illumination-compensation process for reducing the effect of brightness on the image. And then, we analyze the face region based on color in the YCbCr space to obtain the skin color. Also, we try the morphological image processing called closing algorithm to improve the detection. Experimental results demonstrate the effectiveness of our face detection algorithm that leads to 96.7 % precision ratio on average.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.551-557
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• 2013

In intelligent robot research, hand gesture recognition has been an important issue. And techniques that recognize simple gestures are commercialized in smart phone, smart TV for swiping screen or volume control. For gesture recognition, robust hand detection is important and necessary but it is challenging because hand shape is complex and hard to be detected in cluttered background, variant illumination. In this paper, we propose efficient hand detection algorithm for detecting pointing hand for recognition of place where user pointed. To minimize false detections, ROIs are generated within the compact search region using skin color detection result. The ROIs are verified by HOG-SVM and pointing direction is computed by both detection results of head-shoulder and hand. In experiment, it is shown that proposed method shows good performance for hand detection.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.813-816
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• 2005

Ear detection is an important part of an non-invasive ear recognition system. In this paper we propose human ear detection from side face images. The proposed method is made by imitating the human recognition process using feature information and color information. First, we search face candidate area in an input image by using 'skin-color model' and try to find an ear area based on edge information. Then, to verify whether it is the ear area or not, we use the SVM (Support Vector Machine) based on a statistical theory. The method shows high detection ratio in indoors environment with stable illumination.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.821-829
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• 2004

A face recognition has been used for verifying and authorizing valid users, but its applications have been restricted according to lighting conditions. In order to minimizing the restricted conditions, this paper proposes a new algorithm of detecting the face from the input image obtained under the irregular lighting condition. First, the proposed algorithm extracts an edge difference image from the input image where a skin color and a face contour are disappeared due to the background color or the lighting direction. In the next step, it extracts a face region using the histogram of the edge difference image and the intensity information. Using the intensity information, the face region is divided into the horizontal regions with feasible facial features. The each of horizontal regions is classified as three groups with the facial features(including eye, nose, and mouth) and the facial features are extracted using empirical properties of the facial features. Only when the facial features satisfy their topological rules, the face region is considered as a face. It has been proved by the experiments that the proposed algorithm can detect faces even when the large portion of face contour is lost due to the inadequate lighting condition or the image background color is similar to the skin color.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1149-1156
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• 2015

This paper proposes the hand contour detector which is robust to noises. Existing methods reduce noises by applying morphology to extracted edges, detect finger tips by using the center of hands, or exploit the intersection of curves from hand area candidates based on J-value segmentation(JSEG). However, these approaches are so vulnerable to noises that are prone to detect non-hand parts. We propose the noise tolerant hand contour detection method in which non-skin area noises are removed by applying skin area detection, contour detection, and a threshold value. By using the implemented system, we observed that the system was successfully able to detect hand contours.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.72-79
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• 2002

This paper presents an efficient face detection algorithm for color images with a complex background. The presented algorithm utilizes the color information and eigenface that is calculated by PCA-LDA (Principle Component Analysis - Linear Discriminant Analysis). The method of using the color information is faster than any other methods. Eigenface includes average information of the whole test faces. Therefore eigenface can decide that the candidate region is a face. The whole process is composed of two steps. First, it finds first face candidates region of skin tone using a color information in image. We can get a size and position of face candidate region. Second, we compare first face candidate region with eigenface, so decide that an image whether include a face or not. The advantages of the proposed approach include that increasing the detection speed by deciding a size and position of first face candidates region. Also, Betting 97% of the detection rate by comparing the eigenfaces calculated in PCA-LDA.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.210-217
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• 2022

Chemical warfare agents(CWA) such as nerve agents and vesicating agents show lethality by skin contamination. Skin protection, therefore, is one of the top priorities to deal with the growing threat from CWA. In an attempt to develop the most effective topical skin protectant(TSP), candidate substances including PFPE(perfluorinated polyether), PTFE(polytetrafluoroethylene), glycerin, and polysaccharides were evaluated in forms of various formulations against nerve agent simulant DMMP(dimethylmethyl phosphonate) penetration. The protective efficacy of the formulation against DMMP penetration was estimated as the onset time of color change of the KM9 chemical agent detection paper. Based on this study, it was found that several PFPE- and glycerin-based formulations exhibit remarkably superior efficacy as a protective cream. This protective cream is expected to be used as TSP for military application after further research.