• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.35-44
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• 2010

In this paper, we propose a method about producing skin color model adaptively in input image and face detection. The principle process which we proposed is finding eyes candidates by applying the eye features to neural network, and then using the around color to find the distribution of color value. There will be a verification process that producing face region by using color value distribution which is detected as skin region and find mouth candidate in corresponding face region; if eye candidate and mouth candidate's connection structure is similar with face structure, then it can be judged as a face. Because this method can detect skin region adaptively by finding eyes, we solve the rate of false positive about the distorted skin color which is used by existing face detection methods. The experiment was performed about detecting the eye, the skin, the mouth and the face individually. The results revealed that the proposed technique is better than the traditional techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.350-356
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• 2017

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.277-278
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• 2012

본 논문에서는 색상 군집화를 이용한 입술탐지 알고리즘을 제안한다. 이를 위해 이미 많이 알려져 있는 AdaBoost를 이용한 얼굴탐지를 수행한다. 탐지된 얼굴영역에 Lab 컬러시스템을 적용 시킨 후 입술픽셀의 특징에 따른 색상 마커를 사용하여 피부영역을 추출한다. 추출된 피부영역에 대하여 K-means 색상 군집화를 통해 입술영역을 추출한다. 그리고 실험을 통해 입술탐지 결과를 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1414-1419
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• 2015

A method for detecting a skin region on the image is generally used to detect the color information. However, If saturation lowered, skin detection is difficult because hue information of the pixels is lost. So in this paper, we propose a method of correcting color of lower saturation of skin region images by the lighting. Color correction process of this method is saturation image acquisition and low-saturation region classification, segmentation, and the saturation of the split in the low saturation region extraction and color values, the color correction sequence. This method extracts the low saturation regions in the image and extract the color and saturation in the region and the surrounding region to produce a color similar to the original color. Therefore, the method of extracting the low saturation region should be correctly preceding. Because more accurate segmentation in the process of obtaining a low saturation regions, we use a multi-threshold method proposed Otsu in Hue values of the HSV color space, and create a binary image. Our experimental results for 170 portrait images show a possibility that the proposed method could be used efficiently preprocessing of skin color detection method, because the detection result of proposed method is 5.8% higher than not used it.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.19-30
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• 2009

In this paper, We propose a method with which we can detect facial components and face in input image. We use eye map and mouth map to detect facial components using eyes and mouth. First, We find out eye zone, and second, We find out color value distribution of skin region using the color around the eye zone. Skin region have characteristic distribution in YCbCr color space. By using it, we separate the skin region and background area. We find out the color value distribution of the extracted skin region and extract around the region. Then, detect mouth using mouthmap from extracted skin region. Proposed method is better than traditional method the reason for it comes good result with accurate mouth region.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.609-614
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• 2009

The purpose of this paper is to design the metal object detection system for drive inside protection. To do this, we propose the algorithm for designing the color filter that can detect the metal object using fuzzy theory and the algorithm for detecting area of the driver's face using fuzzy skin color filter. Also, by using the proposed algorithm, we propose the algorithm for detecting the metallic object candidate regions. And, the metallic object color filter is then applied to find the candidate regions. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.443-450
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• 2009

This paper develops a novel technique of face detection using brightness distribution in the surrounding area of eye. The proposed face detection consists of facial component candidate extraction, facial component candidate filtering through eye-lip combination, left/right eye classification using brightness distribution, face verification confirming edges in nose region. Because the proposed technique don't use any skin color, it can detect multiple faces in color images with complicated backgrounds and different illumination levels. The experimental results reveal that the proposed technique is better than the traditional techniques in terms of detection ratio.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.37-43
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• 2014

In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5834-5840
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• 2013

Along with the development of high-speed, wired and wireless Internet technology, various harmful images in a form of photos and video clips have become prevalent these days. In this paper, we suggest a method of automatically detecting adult images by extracting woman's nipple areas which represent obscenity of the image. The suggested algorithm first segments skin color areas in the $YC_bC_r$ color space from input images and extracts nipple's candidate areas from the segmented skin areas through the suggested nipple map. We then select real nipple areas by using geometrical information and determines input images as harmful images if they contain nipples. Experimental results show that the suggested nipple map-based method effectively detects adult images.