• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.33-38
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific race image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based fare model.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.193-196
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• 2000

In this paper, we present a model based 3d facial modeling method for virtual reality application using only one front of face photography. We extract facial feature using facial photography and modify mesh of the basic 3D model by the facial feature. After this , We use texture mapping for more similarity. By experiment, we know that the modeling technic is useful method for Movie, Virtual Reality Application, Game , Clothing Industry , 3D Video Conference.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.25-28
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• 2002

This paper introduce a personalized facial expression recognition system. Many previous works on facial expression recognition system focus on the formal six universal facial expressions. However, it is very difficult to make such expressions for normal person without much effort and training. And in these days, the personalized service is also mainly focused by many researchers in various fields. Thus, we Propose a novel facial expression recognition system with fuzzy neural networks and robust image processing.

• Archives of Plastic Surgery
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• v.34 no.4
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• pp.455-460
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• 2007

Purpose: Aging society was realized after persons over 65 was rated above 7% in 2000. It is inevitable fact that society gets older. Few study about facial bone fracture in elderly was reported until now. This study provides a retrospective statistical analysis of facial bone fracture and reports of some demographical information from medical records. Methods: From January 2000 to December 2005, 123 cases of facial bone fracture in above 55 year-old persons were reviewed and analysed. Statistic data was related to distribution, age, sex, causes, occupations, occurrence, time, incidence of facial bone fracture, treatment and it's complications. Results: Facial bone fractures in elderly tend to increase and rated to 4.7%. Facial bone fractures in elderly were most frequently occurred in farmers, cultivator accidents and zygoma fractures. A few minor complications were checked, but easily improved. Conclusion: Facial bone fractures in elderly have small proportion of the whole facial bone fractures, but gradually have been increased. This study was observed trends in changes of facial bone fracture in elderly for 5 years and expected to provide statistical index to prevent facial bone fracture in elderly.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.631-642
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• 2003

We propose an approach to extract and to classify facial features into some classes from one's photo as prepared classification standards to generate one's avatar. Facial Feature Extraction and Classification was executed at eyes, nose, lips, jaw separately and I presented each facial features and classification standards. Extracted Facial Features are used for calculation to features of professional designer's facial component images. Then, most similar facial component images are mapped onto avatar's vector face.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.23-31
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• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1513-1517
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• 2005

In this paper, I make use of a Multi-Channel skin color model with Hue, Cb, Cg using Red, Blue, Green channel altogether which remove bight component as being consider the characteristics of skin color to do modeling more effective to a facial skin color for extracting a facial area. 1 used efficient HOLA(Higher order local autocorrelation function) using 26 feature vectors to obtain both feature vectors of a facial area and the edge image extraction using Harr wavelet in image which split a facial area. Calculated feature vectors are used of date for the facial recognition through learning of neural network It demonstrate improvement in both the recognition rate and speed by proposed algorithm through simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.101-111
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• 1994

The human face can provide a great deal of information in regard to his/her race, age, sex, personality, feeling, psychology, mental state, health condition and ect. If we pay a close attention to the aging process, we are able to find out that there are recognizable phenomena such as eyelid drooping, cheek drooping, forehead furrowing, hair falling-out, the hair becomes gray and etc. This paper proposes that the method to estimate the age by analyzing these feature components for the facial image. Ang we also introduce the method of facial image synthesis in accordance with the cange of age. The feature components according to the change of age can be obtainec by dividing the facial image into the 3-dimensional shape of a face and the texture of a face and then analyzing the principle component respectively using 3-dimensional model. We assume the age of the facial image by comparing the extracted feature component to the facial image and synthesize the resulted image by adding or subtracting the feature component to/from the facial image. As a resurt of this simulation, we have obtained the age changed ficial image of high quality.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.20 no.3
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• pp.147-160
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• 2007

Background and Objective : The facial nerve grading system proposed by House and Brackmann is most widely accepted for the clinical assessment of facial nerve injury. Because of the limitations and subjectivity of the House-Brackmann scale, several new scales of varying degrees of objectivity and ease of use have been introduced. To assess methods of evaluating the function of the facial nerve that have been introduced over the past 20 years, We compared with the House-Brackmann scale. Method : We referred to the information through Entrez Pubmed and Korean studies information(KSI) from 1985 to 2006 about methods of evaluating facial nerve function. We choose 7 scales that focused on objective and easy of use. Result and conclusion : Sunnybrook scale is a weighted, subjective scale with incorporation of secondary defects into a single composite score. Sunnybrook scale can be recommended over House-Brackmann scale.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.