• 제어로봇시스템학회논문지
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• 제12권3호
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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.

• Archives of Plastic Surgery
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• 제38권6호
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• pp.761-764
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• 2011

Purpose: Pediatric facial laceration takes a huge part of patients visiting emergency room and generates social attention for its proper emergency care. So much more attention should be paid to the proper treatment at emergency care unit, and furthermore, thorough survey of background information of the pediatric facial laceration may offer more proper prevention. Methods: According to annual reports of 2009 and 2010, out of 5149 facial laceration patients who were given primary medical care at our clinic, 1452 patients were aged under 15 years old. Retrospective analysis of each pediatric facial lacerations were evaluated according to gender, age, periodic table, cause of injury, place of injury, sites of injury and so on. Results: Pediatric facial laceration was found to occur mostly at 1 year old as they learn to walk and explore their environment. Evaluated analysis revealed that pediatric facial accidents occurred mostly on forehead region (75%), on Sundays, from 5 p.m. to 8 p.m., at home (61.5%). Most common cause of injury was collision (54.5%). Conclusion: In large group of pediatric facial laceration cases provided us with an surprising fact that accidents most commonly occur under parental supervision. This fact gives an actual understanding regarding pediatric facial laceration and more realistic approach in its prevention strategy.

• 대한치과교정학회지
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• 제18권1호
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• pp.235-252
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• 1988

The quadrilateral analysis is a proportional analysis which evaluates the skeletal configuration of lower face on the relations between both jaws in the horizontal as we]1 as vortical dimensions. This study was undertaken to analyse the harmony and disharmony of quadrilateral patterns in normal occlusion and malocclusion. The present study was carried out on lateral cephalograms of 530 Korean children; the subjects consisted of 135 normal occlusions (63 male and 72 female), 105 Class II division 1 malocclusions (52 male and 53 female), 109 Class III malocclusions (50 male and 59 female), 91 hypodivergent facial types (44 male and 47 female) and 90 hyperdivergent facial types (45 male and 45 female). The following conclusions were reached: 1. Means and standard deviation in each group and sex were obtained from normal occlusion and malocclusion. 2. Quadrilateral mean diagram in normal occlusion was constructed for male and female, respectively. 3. In normal occlusion, 1:1 ratio exists between the maxillary base length (A' to Ptm') and mandibular base length (B' to J'), but lower facial height is targer than above. 4. Difference is effective to estimate the degrees of Class II and Class III malocclusion, and lower facial height (LFH) and sagittal angle is effective to recognize the hypodivergent and hyperdivergent facial type. 5. Quadrilateral analysis is able to visualize the anteroposterior and vertical dysplasia of lower face, and it is helpful to recognize certain problems in malocclusion.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권5호
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• pp.251-260
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• 2016

얼굴 인식은 얼굴 영상에서 특징을 추출하고, 이를 다양한 알고리즘을 통해 학습하여 학습된 데이터와 새로운 얼굴 영상에서의 특징과 비교하여 사람을 인식하는 기술로 인식률을 향상시키기 위해서 다양한 방법들이 요구되는 기술이다. 얼굴 인식을 위해 학습 단계에서는 얼굴 영상들로 부터 특징 성분을 추출해야하며, 이를 위한 기존 얼굴 특징 성분 추출 방법에는 선형판별분석(Linear Discriminant Analysis, LDA)이 있다. 이 방법은 얼굴 영상들을 고차원의 공간에서 점들로 표현하고, 클래스 정보와 점의 분포를 분석하여 사람을 판별하기 위한 특징들을 추출하는데, 점의 위치가 얼굴 영상의 화소값에 의해 결정되므로 얼굴 영상에서 불필요한 영역 또는 변화가 자주 발생하는 영역이 포함되는 경우 잘못된 얼굴 특징이 추출될 수 있으며, 특히 일반 카메라 영상을 사용하여 얼굴인식을 수행하는 경우 얼굴과 카메라간의 거리에 따라 얼굴 크기가 다르게 나타나 최종적으로 얼굴 인식률이 저하된다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 일반 카메라를 이용하여 얼굴 영역을 검출하고, 검출된 얼굴 영역에서 Gabor Filter를 이용하여 계산된 얼굴 외곽선을 통해 불필요한 영역을 제거한 후 일정 크기로 얼굴 영역 크기를 정규화하였다. 정규화된 얼굴 영상을 선형 판별 분석을 통해 얼굴 특징 성분을 추출하고, 인공 신경망을 통해 학습하여 얼굴 인식을 수행한 결과 기존의 불필요 영역이 포함된 얼굴 인식 방법보다 약 13% 정도의 인식률 향상이 가능하였다.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.323-333
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• 2023

Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2008년도 학술대회 1부
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• Journal of Korean Neurosurgical Society
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• 제60권2호
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• pp.165-173
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• 2017

Objective : To explore and analyze the influencing factors of facial nerve function retainment after microsurgery resection of acoustic neurinoma. Methods : Retrospective analysis of our hospital 105 acoustic neuroma cases from October, 2006 to January 2012, in the group all patients were treated with suboccipital sigmoid sinus approach to acoustic neuroma microsurgery resection. We adopted researching individual patient data, outpatient review and telephone followed up and the House-Brackmann grading system to evaluate and analyze the facial nerve function. Results : Among 105 patients in this study group, complete surgical resection rate was 80.9% (85/105), subtotal resection rate was 14.3% (15/105), and partial resection rate 4.8% (5/105). The rate of facial nerve retainment on neuroanatomy was 95.3% (100/105) and the mortality rate was 2.1% (2/105). Facial nerve function when the patient is discharged from the hospital, also known as immediate facial nerve function which was graded in House-Brackmann : excellent facial nerve function (House-Brackmann I-II level) cases accounted for 75.2% (79/105), facial nerve function III-IV level cases accounted for 22.9% (24/105), and V-VI cases accounted for 1.9% (2/105). Patients were followed up for more than one year, with excellent facial nerve function retention rate (H-B I-II level) was 74.4% (58/78). Conclusion : Acoustic neuroma patients after surgery, the long-term (${\geq}1year$) facial nerve function excellent retaining rate was closely related with surgical proficiency, post-operative immediate facial nerve function, diameter of tumor and whether to use electrophysiological monitoring techniques; while there was no significant correlation with the patient's age, surgical approach, whether to stripping the internal auditory canal, whether there was cystic degeneration, tumor recurrence, whether to merge with obstructive hydrocephalus and the length of the duration of symptoms.

• 디지털콘텐츠학회 논문지
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• 제8권3호
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• pp.329-339
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• 2007

본 논문은 연속적으로 입력되는 동영상에서 시공간 정보를 이용하여 다양한 조명환경에서도 실시간 적용이 가능한 얼굴영역 검출기법을 제안한다. 제안한 알고리즘은 연속된 두개의 연속 영상에서 에지 차영상을 구하고 연속적으로 입력되는 영상과의 차분 누적영상을 통해 초기 얼굴영역을 검출한다. 초기 얼굴영역으로부터 외부 조명의 영향을 없애기 위해, 검출된 초기 얼굴영역의 수평 프로파일을 이용하여 수직 방향으로 객체영역을 이분하며, 각각의 객체영역에 관해 주색상 밝기를 구한다. 배경과 잡음 성분을 제거한 후, 분할된 얼굴영역을 통합한 주색상 밝기 분포를 이용하여 타원으로 근사화 함으로써 정확한 얼굴의 기울기와 영역을 실시간으로 계산한다. 제안된 방법은 다양한 조명조건에서 얻어진 동영상을 이용하여 실험되었으며 얼굴의 좌 우 기울기가 $30^{\circ}$이하에서 우수한 얼굴영역 검출 성능을 보였다.

• 복식
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• 제64권3호
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• pp.62-76
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• 2014

The purpose of this study is to classify the facial images and analyze shape characteristics of Korean women in their 20s. Previous research and survey were used for the study, the surveys targeted 220 university students in their 20s. The subjects of the experiment were 20-24 year-old Korean women. SPSS 12.0 statistics program was used to analyze the results, and factor analysis, Cronbach's ${\alpha}$ reliability analysis, and multidimensional scaling(MDS) were executed. The results of the study are as follows: First, the facial image types of Korean women in their 20s were classified into 4 categories as 'Youthfulness', 'Classiness', 'Friendliness', and 'Activeness'. Second, the multi-dimensional scaling method was performed and two orthogonal dimensions for the facial image of the Korean women were suggested: strong - soft and classy-friendly. Third, by analyzing the basic statistics concerning the structural characteristics of facial image of Korean women, there were differences in structural characteristics that form the facial images. Especially, significant difference appeared in items related forehead, eyebrows, eyes and jaw.