• Maxillofacial Plastic and Reconstructive Surgery
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• v.34 no.1
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• pp.26-33
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• 2012

Purpose: The golden ratio has been used for a long time to objectify and quantify 'beauty'. Dr. Marqurardt claims that the golden ratio can be applied in the maxillofacial field as well. The purpose of this study was to evaluate the diagnostic significance of using a facial 'phi' mask for analyzing Korean faces with characteristics of Class I, II, and III malocclusion. Methods: We studied twenty five Korean celebrities' frontal facial photos (10 males, 15 females) and 90 malocclusion patients' frontal facial photos (30 patients in each malocclusion classification: Class I, Class II, and Class III). Patients who received orthodontic treatment at Samsung Medical Center were selected for this study. After superimposition of the selected facial photo and facial 'phi' mask using Adobe Photoshop CS3, the ratio of the entire facial area, mid facial area, lower facial area and horizontal and vertical lengths were measured. Results: The facial ratio in photos of Korean faces showed larger vertical and horizontal ratios than the facial 'phi' mask with golden ratio, regardless of skeletal malocclusion (entire face: 115%, lower face: 125% larger than the mask). The results of the frontal photos of Class I, II, and III malocclusion patients using facial 'phi' mask showed that the vertical length and frontal face area was more significantly influenced by the area of the lower face than the midface. This means that the lower face has larger proportions in the facial areas. Conclusion: The ratio of facial 'phi' mask is matched with the ideal facial appearance that the contemporary Korean general public is seeking. Thus, the facial 'phi' mask may be a convenient tool for esthetic analysis of Korean faces. Reducing the area of the lower face is esthetically more desirable for almost all Korean people when planning orthognathic surgery.

• 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 the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.98-107
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• 2007

For high-dimensional pattern recognition, such as face classification, the small number of training samples leads to the Small Sample Size problem when the number of pattern samples is smaller than the number of dimensionality. Recently, various LDA-extensions have been developed, including LDA, PCA+LDA, and Direct-LDA, to address the problem. This paper proposes a method of improving the classification efficiency by increasing the number of (sub)-classes through pre-clustering a training set prior to the execution of Direct-LDA. In LDA (or Direct-LDA), since the number of classes of the training set puts a limit to the dimensionality to be reduced, it is increased to the number of sub-classes that is obtained through clustering so that the classification performance of LDA-extensions can be improved. In other words, the eigen space of the training set consists of the range space and the null space, and the dimensionality of the range space increases as the number of classes increases. Therefore, when constructing the transformation matrix, through minimizing the null space, the loss of discriminatve information resulted from this space can be minimized. Experimental results for the artificial data of X-OR samples as well as the bench mark face databases of AT&T and Yale demonstrate that the classification efficiency of the proposed method could be improved.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.492-497
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• 2005

Feature extraction is important to classify data with large dimension such as image data. The representative feature extraction methods lot feature extraction ate PCA, ICA, LDA and MLP, etc. These algorithms can be classified in two groups: unsupervised algorithms such as PCA, LDA, and supervised algorithms such as LDA, MLP. Among these two groups, supervised algorithms are more suitable to extract the features for classification because of the class information of input data. In this paper we suggest a new feature extraction algorithm PCA-FX which uses class information with PCA to extract ieatures for classification. We test our algorithm using Yale face database and compare the performance of proposed algorithm with those of other algorithms.

• Journal of Platform Technology
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• v.8 no.3
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• pp.22-29
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• 2020

The 4th industrial revolution is progressing by country as a mega trend that leads various technological convergence directions in the social and economic fields from the initial simple manufacturing innovation. The epidemic of infectious diseases such as COVID-19 is shifting digital-centered non-face-to-face business from economic operation, and the use of AI and big data technology for personalized services is essential to spread online. In this paper, we analyze cases focusing on the application of artificial intelligence technology, which is a key technology for the effective implementation of the digital new deal promoted by the government, as well as the major technological characteristics of the 4th industrial revolution and describe the use cases in the field of disaster response. As a disaster response use case, AI assistants suggest appropriate countermeasures according to the status of the reporter in an emergency call. To this end, AI assistants provide speech recognition data-based analysis and disaster classification of converted text for adaptive response.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1148-1157
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• 2022

In 2019, COVID-19 changed many people's lives. Among them, metaverse supports non-face-to-face services through various methods, replacing daily tasks. This phenomenon was created and formed like a culture due to the prolonged COVID-19. In this paper, the fun elements used in the existing game were organized to find out the fun factors of the metaverse, and the items and contents were reclassified according to the metaverse with five experts. Classification was classified using reproducibility, sensory fun [graphic, auditory, text, manipulation, empathy, play, perspective], challenging fun [absorbedness, challenging, discovery, thrill, reward, problem-solving], imaginative fun [new story, love, freedom, agency, expectation, change], social fun[rules, competition, social behavior, status, cooperation, participation, exchange, belonging, currency transaction], interactive fun[decision making, communication sharing, hardware, empathy, nurturing, autonomy], realistic fun[sense of unity in reality, easy of learning, adaptation, intellectual problems solving, pattern recognition, sense of reality, community], and creative fun[application, creation, customizing, virtual world].

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.43-52
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• 2022

We can sense somebody's feeling fatigue, which means that fatigue can be detected through sensing human biometric signals. Numerous researches for assessing fatigue are mostly focused on diagnosing the edge of disease-level fatigue. In this study, we adapt quantitative analysis approaches for estimating qualitative data, and propose video analysis models for measuring fatigue state. Proposed three deep-learning based classification models selectively include stages of video analysis: object detection, feature extraction and time-series frame analysis algorithms to evaluate each stage's effect toward dividing the state of fatigue. Using frontal face videos collected from various fatigue situations, our CNN model shows 0.67 accuracy, which means that we empirically show the video analysis models can meaningfully detect fatigue state. Also we suggest the way of model adaptation when training and validating video data for classifying fatigue.

• Archives of Craniofacial Surgery
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• v.25 no.2
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• pp.77-84
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• 2024

Background: The facial artery is an important blood vessel responsible for supplying the anterior face. Understanding the branching patterns of the facial artery plays a crucial role in various medical specialties such as plastic surgery, dermatology, and oncology. This knowledge contributes to improving the success rate of facial reconstruction and aesthetic procedures. However, debate continues regarding the classification of facial artery branching patterns in the existing literature. Methods: We conducted a comprehensive anatomical study, in which we dissected 102 facial arteries from 52 embalmed and formaldehyde-fixed Vietnamese cadavers at the Anatomy Department, University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam. Results: Our investigation revealed eight distinct termination points and identified 35 combinations of branching patterns, including seven arterial branching patterns. These termination points included the inferior labial artery, superior labial artery, inferior alar artery, lateral nasal artery, angular artery typical, angular artery running along the lower border of the orbicularis oculi muscle, forehead branch, duplex, and short course (hypoplastic). Notably, the branching patterns of the facial artery displayed marked asymmetry between the left and right sides within the same cadaver. Conclusion: The considerable variation observed in the branching pattern and termination points of the facial artery makes it challenging to establish a definitive classification system for this vessel. Therefore, it is imperative to develop an anatomical map summarizing the major measurements and geometric features of the facial artery. Surgeons and medical professionals involved in facial surgery and procedures must consider the detailed anatomy and relative positioning of the facial artery to minimize the risk of unexpected complications.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.1
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• pp.97-103
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• 2009

The status and the property of a tongue are the important indicators to diagnose one's health like physiological and clinicopathological changes of inner organs. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, classifying tongue coating is inevitable but difficult since the features like color and texture of the tongue coatings and substance have little difference, especially in the neighborhood on the tongue surface. The proposed method has two procedures; the first is to acquire the color table to classify tongue coatings and substance by automatically separating coating regions marked by oriental medical doctors, decomposing the color components of the region into hue, saturation and brightness and obtaining the 2nd order discriminant with statistical data of hue and saturation corresponding to each kind of tongue coatings, and the other is to apply the tongue region in an input image to the color table, resulting in separating the regions of tongue coatings and classifying them automatically. As a result, kinds of tongue coatings and substance were segmented from a face image corresponding to regions marked by oriental medical doctors and the color table for classification took hue and saturation values as inputs and produced the classification of the values into white coating, yellow coating and substance in a digital tongue diagnosis system. The coating regions classified by the proposed method were almost the same to the marked regions. The exactness of classification was 83%, which is the degree of correspondence between what Oriental medical doctors diagnosed and what the proposed method classified. Since the classified regions provide effective information, the proposed method can be used to make an objective and standardized diagnosis and applied to an ubiquitous healthcare system. Therefore, the method will be able to be widely used in Oriental medicine.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.69-75
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• 2014

Existing People Counting System using a single overhead mounted camera has limitation in object recognition and counting in various environments. Those limitations are attributable to overlapping, occlusion and external factors, such as over-sized belongings and dramatic light change. Thus, this paper proposes the new concept of People Counting System by Facial Age Group using two depth cameras, at overhead and frontal viewpoints, in order to improve object recognition accuracy and robust people counting to external factors. The proposed system is counting the pedestrians by five process such as overhead image processing, frontal image processing, identical object recognition, facial age group classification and in-coming/out-going counting. The proposed system developed by C++, OpenCV and Kinect SDK, and it target group of 40 people(10 people by each age group) was setup for People Counting and Facial Age Group classification performance evaluation. The experimental results indicated approximately 98% accuracy in People Counting and 74.23% accuracy in the Facial Age Group classification.