• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2349-2357
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• 1994

This paper presents a facial expression transformation algorithm and drawing rule generation algolithm for a portrait drawing robot which was developed for the '93 Taejeon EXPO. The developed algorithm was mainly focused on the robust automatic generation of robot programs with the consideration that the drawing robot should work without any limitation of the age, sex or race for the persons. In order to give more demonstratin effects, the facial expression change of the pictured person was performed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.11-16
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• 2003

This Paper extracts the edge of main components of face with Gator wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.79-91
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• 2004

Various facial pose detection and recognition has been a difficult problem. The problem is due to the fact that the distribution of various poses in a feature space is mere dispersed and more complicated than that of frontal faces, This thesis proposes a robust pose-expression-invariant face recognition method in order to overcome insufficiency of the existing face recognition system. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-V-Z axes, Second, the input face is mapped by deformable template using this vectors and 3D CANDIDE face model. Final. the mapped face is transformed to frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses, Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses and expressions.

• 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.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.10-10
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• 2003

This paper extracts the edge of main components of face with Gabor wavelets transformation in facial expression images. FCM(Fuzzy C-Means) clustering algorithm then extracts the representative feature points of low dimensionality from the edge extracted in neutral face. The feature-points of the neutral face is used as a template to extract the feature-points of facial expression images. To match point to Point feature points on an expression face against each feature point on a neutral face, it consists of two steps using a dynamic linking model, which are called the coarse mapping and the fine mapping. This paper presents an automatic extraction of feature-points by dynamic linking model based on Gabor wavelets and fuzzy C-means(FCM) algorithm. The result of this study was applied to extract features automatically in facial expression recognition based on dimension[1].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.701-703
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• 2022

In this paper, we present a multi-class image scene classification method based on map learning. We were able to learn from the convolutional neural network model in the dataset, classify facial scene images of multiclass people, and classify the optimized CNN model into the Google image dataset in the experiment with significant results.

• Journal of Fashion Business
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• v.19 no.5
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• pp.34-47
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• 2015

Sexiness is the image of a person who attracts attention regardless of his/her age, and today's society has created a more positive impression by transforming the image into something others consider even more attractive. Consequently, we begin to take interest in makeup, - a means of portraying a good impression. Eyebrow makeup in particular, being at the center of the determining factor of a good facial image, can be thought of as the makeup that can most effectively transform one's image. The purpose of this study was to analyze the difference in perceived sexiness depending on the general perception of eyebrow makeup. This study produced an eyebrow stimulus that applied the average rate of change in an image transformation of different eyebrows in order to raise the objective credibility of the sensibility evaluation so it could determine the figure of influence that eyebrows had on facial impressions. The research results showed that the majority of female university students believed that eyebrows were an effective means of expression in changing facial images and attributed a higher mature and sexy image if the average ratio of change was higher. The study, could also identify that a sexy image was perceived when the average rate of change was between 0.39~0.44. In addition, when the gradient of the shape of the viewers' own eyebrows was low, it could be verified that they perceived an image to be sexy from seeing eyebrows with a relatively high gradient.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.40
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• pp.16.1-16.5
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• 2018

Background: Proliferative verrucous leukoplakia (PVL) is an oral potentially malignant disorder, characterized by multifocal expression, progressive clinical evolution, and a high rate of malignant transformation. Evidence-based information regarding optimal PVL management is lacking, due to the paucity of data. The present report describes a case of PVL associated with HPV-16 infection and epithelial dysplasia treated by diode laser surgery, and the outcome of disease clinical remission over a 2-year follow-up period. Case report: A 61-year-old Caucasian male with oral verrucous hyperkeratosis presented for diagnosis. The lesions were localized on the maxillary gingiva and palatal alveolar ridge. Multiple biopsy specimens have been taken by mapping the keratotic lesion area. Microscopic examination was compatible with a diagnosis of PVL with focal mild dysplasia, localized in the right maxillary gingiva. Polymerase chain reaction (PCR) was done for human papillomavirus (HPV) detection which revealed presence of HPV DNA, and the genotype revealed HPV 16 in the sample. The PVL in the right gingival area was treated on an outpatient basis by excision with a diode laser. This approach resulted in good clinical response and decreased morbidity over a 2-year follow-up period. Conclusions: This case illustrates the benefit of a conservative approach by diode laser treatment than wide surgical excision for management of the PVL lesions associated with mild dysplasia and HPV-16 infection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.