• The korean journal of orthodontics
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• v.22 no.4 s.39
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• pp.881-920
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• 1992

This study was designed to assess profile preferences among Korean youths in the year 1992. Facial esthetics was evaluated by means of silhouette profiles, eliminating the influence of a number of aspects that may affect judgment when normal lateral photographs are used. The main points of preference to be clarified here are as follows. First, on facial convexity, Second, on nasion depth, Third, on mentolabial sulcus depth, Fourth, on the position of upper and lower lips, Fifth, on facial type according to Angle's classification of malocclusion, Sixth, on Song's tangents. The 54 subjects printed in questionnaire as black and white silhouettes were selected from 300 tracings from cephalometric radiographs of people whose age ranging from 11 to 20 years. Photographs of six female subjects were retouched by computer graphic software and printed in color and black/white photographs which were used for adaptation of eyes of participants in selecting profiles in silhouette. They constitute 2 questions. The 54 subjects were grouped as 22 questions, each of them composed of 6 subjects, according to the aspects to be clarified. Twenty four questions in total were asked to assess profile preferences. For the assessment, the profile line, the facial esthetic triangle, Song's tangents, and Angle's classification of malocclusion were introduced. The profile line is composed of 11 component points which are Trichion, Glabella, Nasion, Pronasale, Subnasale, Labrale superius, Stomion, Labrale inferius, Supramentale, Pogonion, and Gnathion. The facial esthetic triangle is composed of 3 tangents: A-tangent which is the tangent of dorsum of nose, B-tangent which is the line passing through Sn and Ls, and C-tangent which is drawn on the turning point of the curve which lies between mentolabial sulcus (Sm) and pogonion (Pg). Angle's classification has 3 types of malocclusion which are Class I, Class II, and Class III. Class II malocclusion is subdivided into Division 1 and Division 2. The participants of the survey were composed of 861 college students (448 male students, 413 female students) whose majors grouped as Fine Arts. Liberal Arts, and Natural Sciences, and whose mean age 21.8 years. The statistics program SPSS/PC + of SPSS Inc. was used to analyze answers of participants. Crosstabulation, Chi-square test, and Kendall test were done. The conclusions are as follows: First, Korean youths have a tendency to prefer the slightly convex face to the flat or concave face. Second, they prefer a moderately deep nasion. Third, they prefer a moderately deep mentolabial sulcus. Fourth, they prefer the position of lips which are near to Ricketts' E-line. The position of the upper lip which is slightly posterior to E-line is preferred. The upper lip which lies too far anterior or posterior to the lower lip is not perferred. Fifth, they prefer most, according to Angle's Classification of Malocclusion, Class I facial profile which has a slight inclination to Class II division 2. The order of preference is Class I, Class II division 2, Class III, and Class II division 1. Sixth, they prefer the type 2 and 3 of Song's tangents. The facial profile within which A-and B-tangent meet is preferred. The facial profile which has Cotangent that .meets with A-tangent slightly posterior to the crossing point of A-and B-tangent or that parallels with B-tangent is preferred.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.878-881
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• 2010

For better diagnosis and prescription in Korean traditional medicine, Lee Je-Ma (1837-1900) created Sasang Constitution classification which was divided into four groups of Taeyangin, Soyangin, Taeumin and Soumin based on both body shape and natural disposition. The purpose of this study was to investigate the characteristics of facial skin parameters (hydration, lipid and pH) on forehead and cheek according to Sasang Constitution classifications of Taeumin, Soyangin and Soumin in Korean. Eighty-nine Korean female subjects were recruited for this study and the average age of them was 19.9${\pm}$0.84 years. The four groups by the Sasang Constitution were classified by questionnaire for the Sasang Constitution classification proposed by Kyung-Hee Oriental Medicine Hospital. Consequently, thirty-eight (42.7%) among the subjects were grouped into Soumin, twenty-nine (32.6%) into Taeumin, twenty (22.5%) into Soyangin and two (2%) into Taeyangin. Taeyangin group was excluded from statistical analysis due to small subjects. Hydration, lipid and pH parameters on forehead and cheek were measured by using non-invasive instruments of Corneometer (CM 825, Schwarzhaup, Germany), Sebumeter (SM 815, Schwarzhaup, Germany) and Skin-pH-meter (pH 905, Schwarzhaup, Germany), respectively. The measurements by the same investigator were performed under standardized condition with a room temperature of $21^{\circ}C$ and a humidity level of 40% to 50%. As a result, hydration (F=25.481, p=.000), lipid (F=5.753, p=.005) and pH (F=5.010, p=.009) of the forehead skin showed significant differences in the order of Taeumin, Soyangin and Soumin. Hydration (F=23.216, p=.000), lipid (F=6.898 p=.002) and pH (F=5.070, p=.008) of the cheek skin showed significant differences in the order of Taeumin, Soyangin and Soumin. In conclusion, facial skin surface seemed to be dependent on Sasang Constitution classification in Korean. These findings indicated that Sasang Constitution classification might be an useful esthetic treatment for caring facial skin in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.395-400
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• 2011

In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.2
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• pp.108-120
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• 2004

Object : Patients with facial asymmetry accompanying mandibular prognathism have various causes and clinical features. So, it is difficult to find a satisfactory treatment method functionally and esthetically. Every traditional classification and interpretation to find etiopathogenesis and/or to establish ideal surgical modality has many limitations because it can't be applied simply to various conditions of patients with facial asymmetry accompanying mandibular prognathism. Therefore, we employ a new classification to interpret more details of the morphologic change of mandible and the spatial change of mandible and maxilla. Materials and Methods : Using panoramic X-ray films, PA cephalograms and submentovertex films of 126 patients diagnosed with facial asymmetry accompanying mandibular prognathism as resources, the following results were gathered after analyzing each characteristics through distributing the patterns according to the morphological mandibular asymmetry and mandibular and maxillary spatial asymmetry. Results : Almost frequency of morphological mandibular asymmetry was shown. In case of condyle-ramus elongation and body elongation group, it's frequency was the highest. Higher frequency of compensating vertical growth was shown on the side of over growing maxilla in case of vertical length difference between left and right condyle-ramus. On the other hand, higher frequency of no compensating vertical growth difference between left and right side was shown in case of no vertical length difference in condyle-ramus. Spatial mandibular asymmetry generally occurred when there was no morphological mandibular asymmetry. Correlation between condyle length difference and condyle-ramus length difference between left and right side was very high, but correlation between condyle length difference and body length difference, and correlation between condyle length difference and body vertical length difference was low. Conclusion : In case of patients with facial asymmetry accompanying mandibular prognathism, it is suggested that various pattern of facial asymmetry is occurred by the independent growth of each unit rather than dependent growth of other unit by major growth unit abnormality. Due to the untypical pattern and the various asymmetry occurring according to the changes of each mandibular growth unit, it is considered that an appropriate surgical method should be searched based on the accurate recognition of the each pattern for patients with facial asymmetry accompanying mandibular prognathism.

• Journal of Acupuncture Research
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• v.30 no.4
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• pp.55-68
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• 2013

Objectives : The purpose of this study is to analyze the distribution, relationship, prognosis and improvement score of idiopathic facial palsy inpatients according to constitutional differentiation ; Sasang constition, Hyungsang classification, Sasang constition combined with Hyungsang classification. Methods : A study was done on 102 patients who were diagnosed and treated as idiopathic facial palsy from April 2012 to Nomember 2012 at the Department of Acupuncture and Moxibution Medicine, Dong-eui Oriental University Hospital. Medical records of inpatinets who underwent facial ENoG, NET test were analyzed. Changes of HBGS grade and Yanagihara's score were also evaluated. We classified inpaients acording to Sasang constitution by consulting to Department of Sasang constitutinal medicine, and to Hyungsang classification(Dam-body, Bangkwang-body) by measuring under medical system of 3D facial shapes(RS-400FL). The constitutional differentiation and general characteristics were initially analyzed, and valuse on ENoG, NET were correlated with constitutinal differentiation. Results of HBGS and Yanagihara's score were also correlated with constitutinal differentiation. Results : 1. Each 39.2 percent of idiopathic facial palsy inpatients were the Taeeumin and Soyangin, 21.6 percent were the Soeumin. 2. 75.5 percent of idiopathic facial palsy inpatients were the Bangkwang-body, 24.5 percent were the Dam-body. 3. 34.3 percent of idiopathic facial palsy inpatinets were the Taeeum-Bangkwang, 21.6 percent were the Soyang-Bangkwang, 19.6 percent were the Soeum-Bangkwang, 17.6 percent were the Soyang-Dam, 4.9 percent were the Taeem-Dam, 2 percent were the Soeum-Dam. 4. By sex, the each ratio of Taeeumin, Soeumin, Bangkwang-body, Taeeum-Bangkwang, Soeum-Bangkwang, Soyang-Bangkwang was higher in female, that of Dam-body, Taeeum-Dam, Soyang-Dam was higher in male. 5. By relations between Sasang constitution and Hyungsang classification, the each ratio of Taeeumin and Soeumin was higher in Bangkwang-body. 6. By values on ENoG and NET, evaluations of HBGS's grade and Yanagihara's score, there were no significant difference. Conclusions : In idiopathic facial palsy inpatients, the proportion of Taeeum and Soyang among the Sasang constitution was higher, that of Dam among the Hyungsang classification was higher, that of Taeeum-Bangkwang among the Sasang combined with Hyungsang was the highest. It would seem that Bangkwang-body, female were closely related to Taeeumin, Soeumin. Also, Dam-body were closely related to male. But when comparing groups, there was no statistically significant difference in prognosis and improvement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.200-203
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• 2021

This paper introduces a smart mirror technology that recognizes a person's facial expressions through image classification among several artificial intelligence technologies and presents them in a mirror. 5 types of facial expression images are trained through artificial intelligence. When someone looks at the smart mirror, the mirror recognizes my expression and shows the recognized result in the mirror. The dataset fer2013 provided by kaggle used the faces of several people to be separated by facial expressions. For image classification, the network structure is trained using convolution neural network (CNN). The face is recognized and presented on the screen in the smart mirror with the embedded board such as Raspberry Pi4.

• 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.

• ETRI Journal
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• v.38 no.2
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• pp.347-355
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• 2016

In face examinations, gender classification (GC) is one of several fundamental tasks. Recent literature on GC primarily utilizes datasets containing high-resolution images of faces captured in uncontrolled real-world settings. In contrast, there have been few efforts that focus on utilizing low-resolution images of faces in GC. We propose a GC method based on a pixel classifier boosting with modified census transform features. Experiments are conducted using large datasets, such as Labeled Faces in the Wild and The Images of Groups, and standard protocols of GC communities. Experimental results show that, despite using low-resolution facial images that have a 15-pixel inter-ocular distance, the proposed method records a higher classification rate compared to current state-of-the-art GC algorithms.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.