• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.4
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• pp.528-539
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• 2014

This study analyzed the influence of self-construal on sociocultural attitude toward physical appearance, body satisfaction, and appearance management behavior through a structural equation model. The empirical study was based on the response of 369 adult females between the ages of 20 and 49 in Seoul. Self-construal was presented as an independent self-construal and interdependent self-construal, respectively. The sociocultural attitude toward physical appearance as an intermediate variable in the research model was composed of two sub-factors that included internalization and awareness. The other (body satisfaction) was measured by two factors (body and face). Appearance management behavior (as a final outcome variable) were composed of various factors that included clothing concern, skin care, hair care, and weight training. The findings of this study were: 1) the effect of independent self-construal on the sociocultural attitude toward physical appearance was not significantly meaningful; however, interdependent self-construal influenced it positively. 2) Sociocultural attitude toward physical appearance appeared to have a negative effect on body satisfaction. 3) The body satisfaction also had a negative effect on appearance management behavior in this study.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.3
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• pp.347-354
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• 2014

The color appearance of a yarn-dyed woven fabric depends on the color of the yarn as well as on the weave structure. Predicting the final color appearance or formulating the recipe is a difficult task, considering the interference of colored yarns and structure variations. In a modern fabric design process, the intended color appearance is attained through a digital color methodology based on numerous color data and color mixing recipes (i.e., color prediction models, accumulated in CAD systems). For successful color reproduction, accurate color prediction models should be devised and equipped for the systems. In this study, the final colors of yarn-dyed woven fabrics were predicted using six geometric-color mixing models (i.e., simple K/S model, log K/S model, D-G model, S-N model, modified S-N model, and W-O model). The color differences between the measured and the predicted colors were calculated to evaluate the accuracy of various color models used for different weave structures. The log K/S model, D-G model, and W-O model were found to be more accurate in color prediction of the woven fabrics used. Among these three models, the W-O model was found to be the best one as it gave the least color difference between the measured and the predicted colors.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.336-343
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• 2021

AI technology is spreading not only to industrial fields, but also to culture, art, and content fields. In this paper, we proposed a system based on AI technology that can automate the process of reproducing contents using characters for movie contents. After creating the basic appearance of the character by using the StyleGAN2 model from the video extracted from the movie contents, analyzing the character's personality and propensity using the extracted dialogue data, it was determined from the contemplative appearance based on the yin-yang and five elements to the character's propensity. Accordingly, the external characteristics are reflected in the character. Using the OpenPose model, a character's motion is created, and the finally generated data is integrated to reproduce the content. It is expected that many movie contents can be reproduced through the study of the proposed system.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.7-16
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• 2003

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-bability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated fur the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.392-409
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• 2016

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• Journal of Fashion Business
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• v.9 no.6
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• pp.1-11
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• 2005

The purpose of this study were to examine how people categorize obese individuals and if they have stereotyping about obese individuals. Twenty-five female volunteer subjects participated in this study. Subjects were undergraduate students in Textiles and Clothing courses at a midwestern university, US. Subjects were asked to give their one-word responses to four statements or questions regarding their impressions of six stimuli. The six stimuli consisted of magazine photographs of women; the magazines were general interest and fashion publications. Subjects then recorded their answers in the boxes for each of the six pictures. As the results, the relevant question as to whether or not more negative attributes would be assigned to the obese model's photographs was confirmed for the Description of Model variable, but not for the Personality of Model or for the Liking the Model variables. There was significant difference in means between the positive and negative descriptions of the Description of Model variable in the direction of negativity toward the obese group seems to confirm that, not only do people categorize others based on appearance, but there was a tendency to favor the average-size group and to view as negative the obese group.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.43-48
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• 2018

This study was conducted to reproduce digital colour based on the lightness of the background and size of the colour stimulus so that colour can be similarly perceived under different conditions. With the evolution of display technologies, display devices of various sizes can now reproduce more accurate colour and enhanced images, thus affecting the overall quality of display images. This study reproduced digital colour by considering the visual characteristics of the digital media environment. To accomplish this, we developed a colour appearance model which distinguishes the properties of foveal and peripheral vision. The proposed model is based on existing research on the lightness of the background and size of the colour stimulus. Based on experimental results, an analysis of variance was performed in order to develop the colour appearance model. The algorithm and modelling were verified based on the proposed model. In addition, to apply this model to display technologies, a practical colour control system and a method for handling complex input images were developed. Through this research, colour conversion errors which might occur when the input image is converted to fit a specific display size are resolved from the perspective of the human visual system. As a result, more accurate colour can be displayed and enhanced images can be reproduced.

• Fashion & Textile Research Journal
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• v.15 no.5
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• pp.777-786
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• 2013

This study identifies the influences of appearance-related consumption behavior according to interpersonal relations and masculinity examined through amediated effect of appearance concerns based on a structural study method. Five hypotheses were established to verify the relationships of interpersonal relations, masculinity and appearance-related consumption behavior. Questionnaires were administered to 201 males in their 20s to 50s living in Daegu South Korea. The SPSS 20.0 package utilized for data analysis included frequency analysis, factor analysis, correlation analysis, regression analysis and Cronbach's ${\alpha}$. This study utilized an Amos 21.0 program, a Confirmative Factor Analysis(CFA)and a Structural Equation Modeling(SEM). The results of the study are as follows: First, the appearance concern showed a partial mediation effect between measurement variables that verified the causal relationship of the structural model. Second, interpersonal relations showed a positive influence on masculinity. Third, masculinity showed a positive influence on the appearance concern. Fourth, masculinity had a positive influence on appearance-related consumption behavior. Fifth, interpersonal relations did not have a significant influence on appearance-related consumption behavior. Sixth, the appearance concern showed a positive influence on appearance-related consumption behavior. The findings of this study can influence a market segmentation strategy by predicting future emotional and new consumption markets strategies for male's appearance-related product.