• Proceedings of the SCSK Conference
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• 1992.09a
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• pp.36-45
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• 1992

The dosal sites of 61 Koreans were exposed to increasing UVB doses in the 10mJ/$\textrm{cm}^2$ interval from 10mJ/$\textrm{cm}^2$ to 100mJ/$\textrm{cm}^2$ by solar-simulator, and at the 24hr after UV-B exposure we determined the first MED (Minimal Erythema Dose). For the precise measurements, we peformed the second exposures arround the first MED with 2.5mJ/$\textrm{cm}^2$ interval. The mean MED of all volunteers was 39.6 $\pm$ 15.7mJ/$\textrm{cm}^2$. As the distinction of sex, the means of male and female were 46.9 $\pm$ 18.1mJ/$\textrm{cm}^2$ and 31.5$\pm$8.5mJ/$\textrm{cm}^2$ respectively. According to the classification of skin type by Greiter. F.(1), the percent of skin type I to Ⅵ were 10.9%, 36.4%, 34.6%, 12.7%, 3.6%, and 1.8% respectively.

• Journal of Sasang Constitutional Medicine
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• v.4 no.1
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• pp.187-192
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• 1992

Skin conductance level (SCL) is used to analyze the individual differences in pathological states. The points of measurement were on the back of the second metacarpals, according to the Zhang's biological whole body medical treatment method. SCL was measured in 38 healthy men and women and in 98 patients in lung, liver, stomach, kidney, and paralysis. The classification of the physical constitution was done to them with respect to Lee's physical constitution theory. The following tendencies were observed. 1. SCL in patients group is lower than that in healthy group. 2. In healthy group, SCL in micro-positive physical constitution is higher than those of micro-negative and macro-negative physical constitution. 3. In patients group, SCL in micro-positive physical constitution is lower than those of micro-negative and macro-negative physical constitution vice versa.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.1
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• pp.121-132
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• 2018

Recent beauty trends focus on suitability to individual features. A personal color system is a recent aesthetic concept that influences color make up and coordination. However, a personal color concept has several weaknesses. For example, type classification is qualitative and not quantitative because its measuring system is a sensory test with no industry standard of personal color system. A quantitative personal color type classification model is the purpose of this study, which can be a solution to above problems. This model is a kind of mapping system in a 3D Cartesian coordinate system which has own axes, Value, Saturation, and Yellowness. The cheek color of the individual sample is also independent variable and personal color type is a dependent variable. In order to construct the model, this study conducted a colorimetric survey on a 993 sampling frequency of Korean women in their 20s and 30s. The significance of this study is as follows. First, through this study, personal color system is established on quantitative color space; in addition, the model has flexibility and scalability because it consisted of independent axis that allows for the inclusion of any other critical variable in the form of variable axis.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.637-640
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• 2018

The paper proposes a new deep network-based model that rates periorbital wrinkles in order to alleviate the shortcomings of the evaluation by human experts as well as to facilitate the automation. Periorbital wrinkles still need to be classified by human experts. Furthermore, the classification results from experts are different from each other in many cases due to the inter-interpreter variability and the absence of quantification criteria. Unlike existing classification methods which classify original images, the proposed model consists of a cascade of two deep networks: U-Net for the enhancement of wrinkles on an input image and VGG16 for final classification based on the wrinkle information. Experiments of the proposed model are made with a data set that consists of 433 images rated by experts, showing the promising performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3452-3459
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• 2012

The purpose of this study was to analyze the relationships on participation on health education program toward skin care and the improvement of healthy life habit. The subject of the study was selected by multi-stage stratified cluster random sampling among the skin care education center to learn health education program toward skin care among women in the Seoul and Kyunggi area. The data were collected through a questionnaire adapted from Payne and Hahn's(1986) 'Understanding Your Health-A personal profile; Evaluating Your Health'. The pilot test was executed after the questionnaire was translated into Korean. The statistics employed the study were validity and reliability test, $x^2$ verification, frequency, ANOVA, multiple classification analysis, ANCOVA and Multiple Regression Analysis. The results that were derived from these processes were as follows: First, before and after on health education program toward skin care, the student's healthy life habit is partially changed. Second, the relationship the period of education program and the characteristic healthy life habit, long-term skin care education is positively affected sleeping habit, anti-stress treatment, exercise and nutrition. Third, the relationship on the frequency of education program and the characteristic healthy life habit, more frequently participation on skin care education is positively affected on healthy life habit and exercise.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.75-81
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• 2007

The need for the study of the revealing Sasang constitution at scientific term is increasing as the application of this discipline to the patient produces more accurate result. To obtain scientific evidence of Sasang constitution, it is crucial to analyze accumulated clinical information and associate them to the biological indices that may classify Sasang constitution. Thus, the analysis of clinical information is the most important stepping stone to go toward to the stage of developing model and decision support system (DSS) for classifying Sasang constitution. This study is a preliminary analysis of 1,109 samples collected with 171 clinical indices. To find meaningful clinical indices for classifying Sasang constitutional medicine, we applied decision tree model for them. The skin of 66.5% within whole Taeeumin is thick and non feeble. In the case of 69.8% within whole Soyangin, the skin is non feeble and slippery. In the case of 64.4% within whole Soeumin. they have feeble skin. Therefore, the property of skin can be suggested to be more important than any other index for the classification of Sasang constitution.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.365-374
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• 2011

Recently, malicious video classification and filtering techniques are of practical interest as ones can easily access to malicious multimedia contents through the Internet, IPTV, online social network, and etc. Considerable research efforts have been made to developing malicious video classification and filtering systems. However, the malicious video classification and filtering is not still being from mature in terms of reliable classification/filtering performance. In particular, the most of conventional approaches have been limited to using only the spatial features (such as a ratio of skin regions and bag of visual words) for the purpose of malicious image classification. Hence, previous approaches have been restricted to achieving acceptable classification and filtering performance. In order to overcome the aforementioned limitation, we propose new malicious video classification framework that takes advantage of using both the spatial and temporal features that are readily extracted from a sequence of video frames. In particular, we develop the effective temporal features based on the motion periodicity feature and temporal correlation. In addition, to exploit the best data fusion approach aiming to combine the spatial and temporal features, the representative data fusion approaches are applied to the proposed framework. To demonstrate the effectiveness of our method, we collect 200 sexual intercourse videos and 200 non-sexual intercourse videos. Experimental results show that the proposed method increases 3.75% (from 92.25% to 96%) for classification of sexual intercourse video in terms of accuracy. Further, based on our experimental results, feature-level fusion approach (for fusing spatial and temporal features) is found to achieve the best classification accuracy.