• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1495-1504
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• 2012

This paper proposes an algorithm to classify and analyze facial features such as eyebrow, eye, mouth and chin based on the geometric features of the face. As a preprocessing process to classify and analyze the facial features, the algorithm extracts the facial features such as eyebrow, eye, nose, mouth and chin. From the extracted facial features, it detects the shape and form information and the ratio of distance between the features and formulated them to evaluation functions to classify 12 eyebrows types, 3 eyes types, 9 mouth types and 4 chine types. Using these facial features, it analyzes a face. The face analysis algorithm contains the information about pixel distribution and gradient of each feature. In other words, the algorithm analyzes a face by comparing such information about the features.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• Design Convergence Study
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• v.16 no.6
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• pp.137-152
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• 2017

This research aims to develop the companion robot experience design for elderly in korea based on needs-function deploy matrix of robot and emotion expression research of robot in multimodal interaction. First, Elder users' main needs were categorized into 4 groups based on ethnographic research. Second, the functional elements and physical actuators of robot were mapped to user needs in function- needs deploy matrix. The final UX design prototype was implemented with a robot type that has a verbal non-touch multi modal interface with emotional facial expression based on Ekman's Facial Action Coding System (FACS). The proposed robot prototype was validated through a user test session to analyze the influence of the robot interaction on the cognition and emotion of users by Story Recall Test and face emotion analysis software; Emotion API when the robot changes facial expression corresponds to the emotion of the delivered information by the robot and when the robot initiated interaction cycle voluntarily. The group with emotional robot showed a relatively high recall rate in the delayed recall test and In the facial expression analysis, the facial expression and the interaction initiation of the robot affected on emotion and preference of the elderly participants.

• Journal of the Korea Computer Graphics Society
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• v.17 no.1
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• pp.9-16
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• 2011

In this paper, we describe the development of a system for generating a 3-dimensional human face and predicting it's appearance as it ages over subsequent years using 3D scanned facial data and photo images. It is composed of 3-dimensional texture mapping functions, a facial definition parameter input tool, and 3-dimensional facial prediction algorithms. With the texture mapping functions, we can generate a new model of a given face at a specified age using a scanned facial model and photo images. The texture mapping is done using three photo images - a front and two side images of a face. The facial definition parameter input tool is a user interface necessary for texture mapping and used for matching facial feature points between photo images and a 3D scanned facial model in order to obtain material values in high resolution. We have calculated material values for future facial models and predicted future facial models in high resolution with a statistical analysis using 100 scanned facial models.

• Journal of Sasang Constitutional Medicine
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• v.19 no.3
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• pp.51-61
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• 2007

1. Objectives The Face is an important standard for the classification of Sasang Constitutions. We are developing 3D Automatic Face Recognition Apparatus(3D-AFRA) to analyse the facial characteristics. This apparatus show us 3D image and data of man's face and measure facial figure data. So We should examine the Measurement of Facial Figure data error of 3D Automatic Face Recognition Apparatus(3D-AFRA) in Software Error Analysis. 2. Methods We scanned face status by using 3D Automatic Face Recognition Apparatus(3D-AFRA). And we measured lengths Between Facial Definition Parameters of facial figure data by Facial Measurement program. 2.1 Repeatability test We measured lengths Between Facial Definition Parameters of facial figure data restored by 3D-AFRA by Facial Measurement program 10 times. Then we compared 10 results each other for repeatability test. 2.2 Measurement error test We measured lengths Between Facial Definition Parameters of facial figure data by two different measurement program that are Facial Measurement program and Rapidform2006. At measuring lengths Between Facial Definition Parameters, we uses two measurement way. The one is straight line measurement, the other is curved line measurement. Then we compared results measured by Facial Measurement program with results measured by Rapidform2006. 3. Results and Conclusions In repeatability test, standard deviation of results is 0.084-0.450mm. And in straight line measurement error test, the average error 0.0582mm, and the maximum error was 0.28mm. In curved line measurement error test, the average error 0.413mm, and the maximum error was 1.53mm. In conclusion, we assessed that the accuracy and repeatability of Facial Measurement program is considerably good. From now on we complement accuracy of 3D-AFRA in Hardware and Software.

• Journal of Trauma and Injury
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• v.26 no.3
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• pp.131-138
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• 2013

Purpose: The aim of this study is to present a retrospective review of patients who had a sudden onset of facial palsy after trauma and who underwent facial nerve decompression. Methods: The cases of 25 patients who had traumatic facial palsy were reviewed. Facial nerve function was graded according to the House-Brackmann grading scale. According to facial nerve decompression, patients were categorized into the surgical (decompression) group, with 7 patients in the early decompression subgroup and 2 patients in the late decompression subgroup, and the conservative group(16 patients). Results: The facial nerve decompression group included 8 males and 1 female, aged 2 to 86 years old, with a mean age of 40.8. In early facial nerve decompression subgroup, facial palsy was H-B grade I to III in 6 cases (66.7%); H-B grade IV was observed in 1 case(11.1%). In late facial nerve decompression subgroup, 1 patient (11.1%) had no improvement, and the other patient(11.1%) improved to H-B grade III from H-B grade V. A comparison of patients who underwent surgery within 2 weeks to those who underwent surgery 2 weeks later did not show any significant difference in improvement of H-B grades (p>0.05). The conservative management group included 15 males and 1 female, aged 6 to 66 years old, with a mean age of 36. At the last follow up, 15 patients showed H-B grades of I to III(93.7%), and only 1 patient had an H-B grade of IV(6.3%). Conclusion: Generally, we assume that early facial nerve decompression can lead to some recovery from traumatic facial palsy, but a prospective controlled study should and will be prepared to compare of conservative treatment to late decompression.

• The korean journal of orthodontics
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• v.18 no.2
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• pp.475-488
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• 1988

Certain malocclusion are associated with specific "facial type," and it is important for the clinician to classify the common facial characteristic of each patient. Because the reaction to treatment mechanics and the stability of the denture is depended upon the analysis of the facial pattern. Basically, there are 3 district facial types or patterns under which almost all malocclusion can be classified. 1. mesofacial is the most average growth. 2. brachyfacial which is a horizontal growth pattern has a week muscle, with dental arch, deep bite. 3. dolichofacial which is a vertical growth pattern has a strong muscle, narrow dental arch, open bite. Brachyfacial pattern show a resistant to mandibular rotation during treatment can accept a more protrusive denture and are prominantly nonextraction, whereas dolichofacial patterns tend to open during treatment require a more retracted denture in order to assure post-treatment stability. Brachyfacial pattern would better treat to use extrusive force system, whereas dolichofacial pattern treat to use intrusive force system with head gear and intermaxillary elastics.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.412-414
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• 2001

In this paper, we describe an real-time facial feature tracker. We only used a general USB PC Camera without a frame grabber. The system has achieved a rate of 8+ frames/second without any low-level library support. It tracks pupils, nostrils and corners of the lip. The signal from USB Camera is YUV 4:2:0 vertical Format. we converted the signal into RGB color model to display the image and We interpolated V channel of the signal to be used for extracting a facial region. and we analysis 2D blob features in the Y channel, the luminance of the image with geometric restriction to locate each facial feature within the detected facial region. Our method is so simple and intuitive that we can make the system work in real-time.

• Journal of the Korean Home Economics Association
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• v.42 no.7
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• pp.165-178
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• 2004

The purpose of this study is to research the facial shape and suggest quantified data for the domestic fashion cordinaton and beauty industry. A measurement survey of 264 female college students aged from 20 to 24, was conducted with photographs of front view and later view taken by digital camera. Two hundred values were generated through the facial measurement program. In the results, for the coefficient of variance of the height was highly related with height, radial length was highly related with radial length, and breadth with highly related with breadth. However Gnation breadth, Zygomatic breadth, Philtrum breadth were lowly related and indefendent with other items.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.