• The KIPS Transactions:PartA
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• v.17A no.6
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• pp.281-288
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• 2010

In this paper, we describe the development of a system for generating a 3-dimensional human face using 3D scanned facial data and photo images, and morphing animation. The system comprises a facial feature input tool, a 3-dimensional texture mapping interface, and a 3-dimensional facial morphing interface. The facial feature input tool supports texture mapping and morphing animation - facial morphing areas between two facial models are defined by inputting facial feature points interactively. The texture mapping is done first by means of three photo images - a front and two side images - of a face model. The morphing interface allows for the generation of a morphing animation between corresponding areas of two facial models after texture mapping. This system allows users to interactively generate morphing animations between two facial models, without programming, using 3D scanned facial data and photo images.

• 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 Digital Contents Society
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• v.9 no.4
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• pp.751-758
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• 2008

The purpose of this study is to implement 3D Face Model, which resembles a user, using 3D Graphic techniques. The implemented 3D Face model is used to further study and implement 3D Facial Aesthetic Surgery System, that can be used to increase the satisfaction rate of patient by comparing before and after facial aesthetic surgery. For designing and implementing 3D Facial Aesthetic Surgery System, 3D Modeling, Texture Mapping for skin, Database system for facial data are studied and implemented independently. The Detailed Adjustment System is, also, implemented for reflecting the minute description of face. The implemented 3D Facial Aesthetic Surgery System for this paper shows more accuacy, convenience, and satisfaction in compare with the existing system.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1187-1193
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• 2013

In this paper, we present realistic 3D head modeling and facial expression systems. For 3D head modeling, we perform generic model fitting to make individual head shape and texture mapping. To calculate the deformation function in the generic model fitting, we determine correspondence between individual heads and the generic model. Then, we reconstruct the feature points to 3D with simultaneously captured images from calibrated stereo camera. For texture mapping, we project the fitted generic model to image and map the texture in the predefined triangle mesh to generic model. To prevent extracting the wrong texture, we propose a simple method using a modified interpolation function. For generating 3D facial expression, we use the vector muscle based algorithm. For more realistic facial expression, we add the deformation of the skin according to the jaw rotation to basic vector muscle model and apply mass spring model. Finally, several 3D facial expression results are shown at the end of the paper.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.705-709
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• 2009

In this paper, we propose 3d face modeling using two orthogonal views of 2D face images and automatically facial feature extraction. Th proposed technique consists of 2 parts, personalization of 3d face model and texture mapping.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.737-744
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• 2009

This paper is to study and implement 3D Virtual Face Aesthetic Surgery System which provides more satisfaction by comparing the before-and-after plastic face surgery using 3D face model. For this study, we implemented 3D Face Model Generating System which resembles 2D image of the user based on 3D Korean standard face model and user's 2D pictures. The proposed 3D Virtual Face Aesthetic Surgery System in this paper consists of 3D Face Model Generating System, 3D Skin Texture Mapping System, and Detailed Adjustment System for reflecting the detailed description of face. The proposed system provides more satisfaction to the medical uses and stability in the surgery in compare with other existing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1690-1710
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• 2014

In this paper, we propose the rapid implementation of a 3-dimensional (3D) facial reconstruction from a single frontal face image and introduce a design for its application on a mobile device. The proposed system can effectively reconstruct human faces in 3D using an approach robust to lighting conditions, and a fast method based on a Canonical Correlation Analysis (CCA) algorithm to estimate the depth. The reconstruction system is built by first creating 3D facial mapping from a personal identity vector of a face image. This mapping is then applied to real-world images captured with a built-in camera on a mobile device to form the corresponding 3D depth information. Finally, the facial texture from the face image is extracted and added to the reconstruction results. Experiments with an Android phone show that the implementation of this system as an Android application performs well. The advantage of the proposed method is an easy 3D reconstruction of almost all facial images captured in the real world with a fast computation. This has been clearly demonstrated in the Android application, which requires only a short time to reconstruct the 3D depth map.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.328-333
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• 2003

This paper did to do with 18 muscle pairs that do fetters in anatomy that influence in facial expression change and mix motion of muscle for face facial animation. After set and change mash and make standard model in individual's image, did mapping to mash using individual facial front side and side image to raise truth stuff. Muscle model who become motive power that can do animation used facial expression creation correcting Waters' muscle model. Created deformed face that texture is dressed using these method. Also, 6 facial expression that Ekman proposes did animation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2681-2690
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• 2010

This paper proposes 3D facial modeling system without using 3D scanner and camera or expensive software. This system enables efficient 3D facial modeling to cost reduction and effort saving for natural facial modeling. It detects edges of component of face using edge detection based on fuzzy logic from any 2D image of front face. It was mapped fitting position with 3D standard face model by detected edge more correctly. Also this system generates 3D face model more easily through floating and flexible control and texture mapping after fitting that connection of control point on detected edge from 2D image and mesh of 3D standard face model.