• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4508-4515
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• 2014

Real-time body detection has been researched actively. On the other hand, the detection rate of color distorted images is low because most existing detection methods use static skin color model. Therefore, this paper proposes a new method for detecting the skin color region using a gradient map and saturation features. The basic procedure of the proposed method sequentially consists of creating a gradient map, extracting a gradient feature of skin regions, noise removal using the saturation features of skin, creating a cluster for extraction regions, detecting skin regions using cluster information, and verifying the results. This method uses features other than the color to strengthen skin detection not affected by light, race, age, individual features, etc. The results of the detection rate showed that the proposed method is 10% or more higher than the traditional methods.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.491-500
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• 2002

In this paper, we propose a real-time face detection and recognition system by using skin color informations, geometrical feature vectors of face, and facial angle informations from color face image. The proposed algorithm improved face region extraction efficiency by using skin color informations on the HSI color coordinate and face edge information. And also, it improved face recognition efficiency by using geometrical feature vectors of face and facial angles from the extracted face region image. In the experiment, the proposed algorithm shows more improved recognition efficiency as well as face region extraction efficiency than conventional methods.

• Food Science of Animal Resources
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• v.43 no.1
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• pp.61-72
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• 2023

The practical use of Korean native black goat skin as a source of gelatin extraction is limited. The objective of this study was to optimize the extraction temperature and time of gelatin from Korean native black goat skin, and to compare the quality characteristics of goat skin gelatin and other commercial gelatin products. Response surface methodology was applied to optimize the extraction temperature and time of gelatin obtained from native Korean black goat skin. The effects of temperature (50℃-70℃) and time (2-4 h) on extraction yield and gel strength were investigated using a face-centered central composite design with 13 experiments. Gelatin extraction from Korean native black goat skin was prepared through the serial processes of alkali pre-treatment, bleaching, neutralization, hot-water extraction, and freeze-drying. Using the optimization plot of Minitab software, the optimized conditions for extracting temperature and time of goat skin gelatin were 59.49℃ and 3.03 h, and the optimized values of extraction yield and gel strength were 12.52% and 263.37 g, respectively. Based on a quality comparison of goat skin gelatin with commercial gelatin, the pH value of gelatin extracted from Korean native black goat skin was 5.57. The color of gelatin extracted from Korean native black goat skin was darker than that of commercial gelatin (p<0.05). Higher emulsifying properties and gel strength of goat skin gelatin were observed when compared to those of commercial gelatin (p<0.05). Therefore, the results of this study indicate that Korean native black goat skin may be a valuable source for gelatin extraction.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.42-48
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• 2000

In this paper, we propose the face image recognition algorithm using skin color information, face region features such as eye, nose, and mouse, etc., and geometrical features of chin line. In the proposed algorithm, we used the intensity as well as skin color information in the HSI color coordinate which is similar to human eye system. The experimental results of proposed method shows improved extraction quality of face and provides adaptive extraction methods for the races. And also, we used chin line information as well as geometrical features of face such as eye, nose, mouse information for the improvement of face recognition quality, Experimental results shows the more improved recognition as well as extraction quality than conventional methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1513-1517
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• 2005

In this paper, I make use of a Multi-Channel skin color model with Hue, Cb, Cg using Red, Blue, Green channel altogether which remove bight component as being consider the characteristics of skin color to do modeling more effective to a facial skin color for extracting a facial area. 1 used efficient HOLA(Higher order local autocorrelation function) using 26 feature vectors to obtain both feature vectors of a facial area and the edge image extraction using Harr wavelet in image which split a facial area. Calculated feature vectors are used of date for the facial recognition through learning of neural network It demonstrate improvement in both the recognition rate and speed by proposed algorithm through simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.55-67
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• 1998

This paper presents an automatic facial feature point extraction algorithm in sequential color images. To extract facial region in the video sequence, a moving color detection technique is proposed that emphasize moving skin color region by applying motion detection algorithm on the skin-color transformed images. The threshold value for the pixel difference detection is also decided according to the transformed pixel value that represents the probability of the desired color information. Eye candidate regions are selected using both of the black/white color information inside the skin-color region and the valley information of the moving skin region detected using morphological operators. Eye region is finally decided by the geometrical relationship of the eyes and color histogram. To decide the exact feature points, the PCA(Principal Component Analysis) is used on each eye and mouth regions. Experimental results show that the feature points of eye and mouth can be obtained correctly irrespective of background, direction and size of face.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.520-523
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• 2009

There are many ways to judge whether the input image is adult-image or not. Until now, adult image detection has been examined by the ratio of skin area in full image. In this paper, we propose a method to extract skin region in YCbCr. Skin region shows unique distribution in YCbCr, and we will separate the skin region from background using the distribution. First, we are going to find Eye zone using Eye-Map. Then we will find out the color value for the distribution of skin region using the color of Eye zone. Next, we will find the distribution of the area through the skin region in full-image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.240-243
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• 2002

In this paper, we propose a face region detection based on skin-color distribution and facial feature extraction algorithm in color still images. To extract face region, we transform color using general skin-color distribution. Facial features are extracted by edge transformation. This detection process reduces calculation time by a scale-down scanning from segmented region. we can detect face region in various facial Expression, skin-color deference and tilted face images.