• 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.

• Archives of Plastic Surgery
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• v.38 no.6
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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.

• The korean journal of orthodontics
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• v.18 no.1 s.25
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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.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.251-260
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• 2016

Face recognition is a technology to extract feature from a facial image, learn the features through various algorithms, and recognize a person by comparing the learned data with feature of a new facial image. Especially, in order to improve the rate of face recognition, face recognition requires various processing methods. In the training stage of face recognition, feature should be extracted from a facial image. As for the existing method of extracting facial feature, linear discriminant analysis (LDA) is being mainly used. The LDA method is to express a facial image with dots on the high-dimensional space, and extract facial feature to distinguish a person by analyzing the class information and the distribution of dots. As the position of a dot is determined by pixel values of a facial image on the high-dimensional space, if unnecessary areas or frequently changing areas are included on a facial image, incorrect facial feature could be extracted by LDA. Especially, if a camera image is used for face recognition, the size of a face could vary with the distance between the face and the camera, deteriorating the rate of face recognition. Thus, in order to solve this problem, this paper detected a facial area by using a camera, removed unnecessary areas using the facial feature area calculated via a Gabor filter, and normalized the size of the facial area. Facial feature were extracted through LDA using the normalized facial image and were learned through the artificial neural network for face recognition. As a result, it was possible to improve the rate of face recognition by approx. 13% compared to the existing face recognition method including unnecessary areas.

• Journal of Information Processing Systems
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• v.19 no.3
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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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• 2005.06a
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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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• 2008.02a
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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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• v.60 no.2
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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.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.329-339
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• 2007

In this paper we present a facial region detection algorithm for real-time image with complex background and various illumination using spatial and temporal methods. For Detecting Human region It used summation of Edge-Difference Image between continuous image sequences. Then, Detected facial candidate region is vertically divided two objected. Non facial region is reduced using Analysis of Major Color Component. Non facial region has not available Major Color Component. And then, Background is reduced using boundary information. Finally, The Facial region is detected through horizontal, vertical projection of Images. The experiments show that the proposed algorithm can detect robustly facial region with complex background various illumination images.

• Journal of the Korean Society of Costume
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• v.64 no.3
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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.