• The Journal of Korean Association of Computer Education
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• v.17 no.2
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• pp.107-113
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• 2014

In this study, we design and implement an eye-gaze estimation system based on the extraction of eye contour and pupil region. In order to effectively extract the contour of the eye and region of pupil, the face candidate regions were extracted first. For the detection of face, YCbCr value range for normal Asian face color was defined by the pre-study of the Asian face images. The biggest skin color region was defined as a face candidate region and the eye regions were extracted by applying the contour and color feature analysis method to the upper 50% region of the face candidate region. The detected eye region was divided into three segments and the pupil pixels in each pupil segment were counted. The eye-gaze was determined into one of three directions, that is, left, center, and right, by the number of pupil pixels in three segments. In the experiments using 5,616 images of 20 test subjects, the eye-gaze was estimated with about 91 percent accuracy.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2429-2434
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• 2010

In this paper, we propose a method to extract and analyze palm line with fuzzy method. In order to extract the palm part, we transform the original RGB color space to YCbCr color space and extract sin colors ranging Y:65-255, Cb:25-255, Cr:130-255 and use it as a threshold. Possible noise is removed by 8-directional contour tracking algorithm and morphological characteristic of the palm. Then the edge is extracted from that noise-free image by stretching method and sobel mask Then the fuzzy binarization algorithm is applied to remove any minute noise so that we have only the palm lines and the boundary of the hand. Since the palm line reading is done with major lines, we use the morphological characteristics of the analyzable palm lines and fuzzy inference rules. Experiment verifies that the proposed method is better in visibility and thus more analyzable in palm reading than the old method.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.465-472
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• 2003

For an effective pre-treatment process of a face input image, it is necessary to detect each of face components, calculate the face area, and estimate the rotary angle of the face. A proposed method of this study can estimate an robust result under such renditions as some different levels of illumination, variable fate sizes, fate rotation angels, and background color similar to skin color of the face. The first step of the proposed method detects the estimated face area that can be calculated by both adapted skin color Information of the band-wide HSV color coordinate converted from RGB coordinate, and skin color Information using histogram. Using the results of the former processes, we can detect a lip area within an estimated face area. After estimating a rotary angle slope of the lip area along the X axis, the method determines the face shape based on face information. After detecting eyes in face area by matching a partial template which is made with both eyes, we can estimate Y axis rotary angle by calculating the eye´s locations in three dimensional space in the reference of the face area. As a result of the experiment on various face images, the effectuality of proposed algorithm was verified.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.6 s.154
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• pp.971-980
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• 2006

The colors of apparel have a close relationship with the facial color types of consumers. To extract the favorable colors that flatter to consumer's facial color types, the facial colors of Korean females were analyzed. With color meter JX-777, 2 points of face were measured and classified into 3 clusters that had similar hue, value and chroma. Other new 10 college girls were measured and 3 subject among them were selected by the criteria that choose new subjects who have the classified facial color types. 175 respondents answered the degree of becomingness of color samples on three subjects. Data were analyzed by K-means cluster analysis, ANOVA and Duncan multiple range test using SPSS Win. 12. Findings were as follows: 1) 324 subjects who had YR facial colors were classified into 3 facial color groups. The average facial color Type 1 was 4.82YR 6.47/3.70 and composed 48.88% among total observations. Type 2 was 5.99YR 6.12/4.12 and 30.25%. Type 3 was 5.15YR 7.07/4.97 and 20.99% respectively. 2) Favorable colors for Type 1 were 18 colors that belonged to 'a' group from among colors that were divided into a, b, c group by Duncan post hoc test. 3) Type 2 showed that this type had many unfavorable colors. Unfavorable colors were 18 colors that belonged to 'c' by Duncan test. 4) Type 3 showed that black is the most favorable color and 18 colors were at middle level, which belonged to 'b' from among 18 colors that were divided into a, b, and c by Duncan test.

• 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 Advanced Navigation Technology
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• v.15 no.6
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• pp.1132-1137
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• 2011

In this paper, we propose Modified Multi CAMShift Algorithm(Modified Multi Continuously Adaptive Mean Shift Algorithm) that extracts skin color area and tracks several human body parts for real-time human tracking system. Skin color area is extracted by filtering input image in predefined RGB value range. These areas are initial search windows of hands and face for tracking. Gaussian background model prevents search window expending because it restricts skin color area. Also when occluding between these areas, we give more weights in occlusion area and move mass center of target area in color probability distribution image. As result, the proposed algorithm performs better than the original CAMShift approach in multiple object tracking and even when occluding of objects with similar colors.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.2 s.150
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• pp.316-325
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• 2006

The colors of apparel are getting more important to give the differentiated character on fiber and fabrics. This study was to extract the favorable colors that become to facial color types. Research was carried out to classify the facial colors into several similar facial color groups. With JX-777, 2 points of face: forehead and cheek, were measured and classified into 3 facial color types. Sample size was 418 Korean adult males and other 15 of new males subjects. New chosen 3 subjects who had the classified facial color types, wore silver gown and black hat on his head to minimize the interaction of the clothe color an hair. The 40 standardized color samples were used to extract the favorable colors. 187 respondents answered the degree of becomingness of color samples on 3 facial color types. Data were analyzed by K-means cluster analysis, ANOVA and Duncan multiple range test using SPSS Win. 12. Findings were as follows: 1. 418 subjects who had YR colors were classified into 3 kinds of facial color groups. Type 1 was 4.59YR 5.89/5.12, Type 2 was 5.61 YR 5.41/4.79 and Type 3 was 4.38YR 6.49/4.89 respectively. 2. Favorable colors for Type 1 were 2 colors that belonged to ' a ' group from among colors that were divided into a, b, c group and 18 colors that belonged to ' a ' group from among colors that were divided into a, b group by Duncan post hoc test. 3. Type 2 showed that this type had many unfavorable colors. Unfavorable colors were 16 colors that belonged to ' c ' by Duncan test. 5. Favorable colors for Type 3 were 14 colors that belonged to ' a ' from among colors that were divided into a, b, c and 16 colors that belonged to ' a ' from among colors that were divided into a, b by Duncan test.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.684-691
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• 2001

In this paper, we design and implement a system which recognizes and tracks a human head on a sequence of images. In this paper, the color of the skin and ellipse modeling is used as feature vectors to recognize the human head. And the modified time-varying edge detection method and the vertical projection method is used to acquire regions of the motion from images with very complex backgrounds. To select the head from the acquired candidate regions, the process for thresholding on the basis of the I-component of YIQ color information and mapping with ellipse modeling is used. The designed system shows an excellent performance in the cases of the rotated heads, occluded heads, and tilted heads as well as in the case of the normal up-right heads. And in this paper, the combinational technique of motion-based tracking and recognition-based tracking is used to track the human head exactly even though the human head moves fast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.20-32
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• 2010

Previous hardware devices to capture human motion have many limitations; expensive equipment, complexity of manipulation or constraints of human motion. In order to overcome these problems, real-time motion capture algorithms based on computer vision have been actively proposed. This paper presents an efficient analysis method of multiple view images for real-time motion capture. First, we detect the skin color regions of human being, and then correct the image coordinates of the regions by using camera calibration and epipolar geometry. Finally, we track the human body part and capture human motion using kalman filter. Experimental results show that the proposed algorithm can estimate a precise position of the human body.