• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.189-194
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• 2004

This paper describes method to distribute much high-dimensional facial expression motion data to 2 dimensional space, and method to create facial expression animation by select expressions that want by realtime as animator navigates this space. In this paper composed expression space using about 2400 facial expression frames. The creation of facial space is ended by decision of shortest distance between any two expressions. The expression space as manifold space expresses approximately distance between two points as following. After define expression state vector that express state of each expression using distance matrix which represent distance between any markers, if two expression adjoin, regard this as approximate about shortest distance between two expressions. So, if adjacency distance is decided between adjacency expressions, connect these adjacency distances and yield shortest distance between any two expression states, use Floyd algorithm for this. To materialize expression space that is high-dimensional space, project on 2 dimensions using Sammon's Mapping. Facial animation create by realtime with animators navigating 2 dimensional space using user interface.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.85-93
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• 2005

This paper describes how to distribute high multi-dimensional facial expression data of vast quantity over a suitable space and produce facial expression animations by selecting expressions while animator navigates this space in real-time. We have constructed facial spaces by using about 2400 facial expression frames on this paper. These facial spaces are created by calculating of the shortest distance between two random expressions. The distance between two points In the space of expression, which is manifold space, is described approximately as following; When the linear distance of them is shorter than a decided value, if the two expressions are adjacent after defining the expression state vector of facial status using distance matrix expressing distance between two markers, this will be considered as the shortest distance (manifold distance) of the two expressions. Once the distance of those adjacent expressions was decided, We have taken a Floyd algorithm connecting these adjacent distances to yield the shortest distance of the two expressions. We have used CCA(Curvilinear Component Analysis) technique to visualize multi-dimensional spaces, the form of expressing space, into two dimensions. While the animators navigate this two dimensional spaces, they produce a facial animation by using user interface in real-time.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.109-111
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• 2006

본 논문은 애니메이터로 하여금 얼굴 표정들의 공간으로부터 일련의 표정을 선택하게 함으로써 3차원 아바타의 표정을 실시간적으로 제어할 수 있도록 하기 위한 표정공간의 생성방법에 관하여 기술한다. 본 시스템에서는 약 2400여개의 얼굴 표정 프레임을 이용하여 표정공간을 구성하였다. 본 기법에서는 한 표정을 표시하는 상태표현으로 얼굴특징 점들 간의 상호거리를 표시하는 거리행렬을 사용한다. 이 거리행렬의 집합을 표정공간으로 한다. 그러나 이 표정공간은 한 표정이 다른 표정까지 이동할 때 두 표정간의 직선경로를 통해 이동할 수 있는 그런 공간이 아니다. 본 기법에서는 한 표정에서 다른 표정까지 거쳐 갈 수 있는 경로를 캡쳐된 표정 데이터로부터 근사적으로 유추한다. 우선, 각 표정상태를 표현하는 거리행렬간의 거리가 일정 값 이하인 경우 두 표정을 인접해 있다고 간주한다. 임의의 두 표정 상태가 일련의 인접표정들의 집합으로 연결되어 있으면 두 표정간에 경로가 존재한다고 간주한다. 한 표정에서 다른 표정으로 변화할 때 두 표정간의 최단경로를 통해 이동한다고 가정한다. 두 표정간의 최단거리를 구하기 위해 다이내믹 프로그래밍 기법을 이용한다. 이 거리행렬의 집합인 표정공간은 다차원 공간이다. 3차원 아바타의 얼굴 표정 제어는 애니메이터들이 표정공간을 항해할 때 실시간적으로 수행된다. 이를 도와주기 위해 표정공간을 차원 스케일링 기법을 이용하여 2차원 공간으로 가시화하였고, 애니메이터들로 하여금 본 시스템을 사용하여 실시간 표정 제어를 수행하게 했는데, 본 논문은 그 결과를 평가한다.참여하는 빈들 간의 관계를 분석하여 워크플로우에 대한 성능 측정이 가능하도록 한다. 또한 제안된 메트릭을 통하여 EJB 어플리케이션의 성능 향상을 도모할 수 있도록 한다.로 표면 위로 자라났고, 부종은 창상 밑 조직까지 감소하였으며, 육아조직은 교원질 섬유로 대체되었다. 창상 유발 21일 후, 다른 창상에 비해, HG 처치창은 유의적으로 창상 표면이 거의 재생성 상피로 덮였으며, 육아조직은 창상 유발 14일 후와 비교해서 유의적으로 교원질 섬유로 대체되었다. 위의 모든 결과에서 보듯이, 개에서 전층피부 창상의 처치 시 HG의 사용은 HC와 생리 식염수에 비해 창상치유 복구기의 치유 속도를 촉진하는 것으로 사료된다.시범학교의 자녀를 들 부모들은 환경관련문제에 대한 의식 및 환경관련 제품에 대한 구매행동의 변화가 두드러지게 나타났다.EX>$d^{2+}$ + SA처리구는 두 화합물의 이중 효과에 의해 전반적인 생리 활성을 억제하여, 결국에는 식물의 고사를 유도하는 것으로 사료된다.목에 대한 보안'이 가장 중요한 것으로 나타났다. 본 연구에서는 솔루션 선정요인에 관한 중요도를 e-마켓플레이스의 유형과 산업 별로 평가해보았는데, 여기에서 밝혀진 중요도를 통해 e 마켓플레이스를 구축하고 자 하는 기업은 솔루션을 자체 개발하거나 구입할 때 올바른 의사결정을 할 수 있다. 그리고 솔루션을 제공하려는 기업측면에서는 e-마켓플레이스를 구축하고자 하는 기업에게 유형과 산업별로 적절한 솔루션을 제공할 수 있다.순환이 뇌대사 및 수술 후 신경학적 결과에 보다 유익한 효과를 제공해 줄 수 있음

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.383-390
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• 2004

This paper propose a method that controls facial expression of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. And we setup its system. The space of expression is created from about 2400 frames consist of motion captured data of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. But this space is not such a space where one state can go to another state via the straight trajectory between them. We derive trajectories between two states from the captured set of expressions in an approximate manner. First, two states are regarded adjacent if the distance between their distance matrices is below a given threshold. Any two states are considered to have a trajectory between them If there is a sequence of adjacent states between them. It is assumed . that one states goes to another state via the shortest trajectory between them. The shortest trajectories are found by dynamic programming. The space of facial expressions, as the set of distance matrices, is multidimensional. Facial expression of 3D avatar Is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the multidimensional scaling(MDS). To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. As a result of that, users estimate that system is very useful to control facial expression of 3D avatar in real-time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.130-133
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• 2004

인간의 감정을 정의하거나 분류하는 것은 매우 어려운 일이다. 이러한 애매모호한 인간의 감정은 어느 한 감정만 나타나는 것이 아니고 다양한 감정의 복합으로, 눈에 띄는 강정이 드러나는 것이다. 인간의 애매모호한 감정 상태와 유사한 감성을 표현하는 알고리즘으로 dynamic emotion Space를 이용한 감성 표현 알고리즘을 제안한다. 기존의 감성 표현 아바타들이 키리 설정된 몇개의 감정만을 데이터 베이스에서 불러와 표현하는 반면에, 본 논문에서 제안하는 감성 표현 시스템은 동적으로 변화하는 감성 공간을 이용하여 감성을 표현함으로써 무수히 다양한 표정을 표현할 수 있다. 실제로 인간의 복합적이고 다양한 표정을 표현할 수 있는지를 알아보기 위해 실제 구현 및 실험을 수행하고, dynamic emotion space를 이용한 감성 표현 시스템의 성능을 입증한다.

• Journal of Korea Multimedia Society
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• v.7 no.10
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• pp.1478-1484
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• 2004

This paper presents a method that controls facial expression in realtime of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. The space of expression is created from about 2400 frames of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. Facial expression of 3D avatar is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the Principal Component Analysis(PCA) projection. To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. This paper evaluates the results.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.117-124
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• 2007

This paper describes methodology that enables animators to create the facial expression animations and to control the facial expressions in real-time by reusing motion capture datas. In order to achieve this, we fix a facial expression state expression method to express facial states based on facial motion data. In addition, by distributing facial expressions into intuitive space using LLE algorithm, it is possible to create the animations or to control the expressions in real-time from facial expression space using user interface. In this paper, approximately 2400 facial expression frames are used to generate facial expression space. In addition, by navigating facial expression space projected on the 2-dimensional plane, it is possible to create the animations or to control the expressions of 3-dimensional avatars in real-time by selecting a series of expressions from facial expression space. In order to distribute approximately 2400 facial expression data into intuitional space, there is need to represents the state of each expressions from facial expression frames. In order to achieve this, the distance matrix that presents the distances between pairs of feature points on the faces, is used. In order to distribute this datas, LLE algorithm is used for visualization in 2-dimensional plane. Animators are told to control facial expressions or to create animations when using the user interface of this system. This paper evaluates the results of the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.18-23
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• 2005

It is difficult to define and classify human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. And among them, a remarkable emotion is expressed. This paper proposes a emotional expression algorithm using dynamic emotion space, which give facial expression in similar with vague human emotion. While existing avatar express several predefined emotions from database, our emotion expression system can give unlimited various facial expression by expressing emotion based on dynamically changed emotion space. In order to see whether our system practically give complex and various human expression, we perform real implementation and experiment and verify the efficacy of emotional expression system based on dynamic emotion space.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.726-734
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• 2004

This paper presents a facial animation method that enables the user to select a sequence of facial frames from the facial expression space, whose level of details the user can select hierarchically Our system creates the facial expression space from about 2400 captured facial frames. To represent the state of each expression, we use the distance matrix that represents the distance between pairs of feature points on the face. The shortest trajectories are found by dynamic programming. The space of facial expressions is multidimensional. To navigate this space, we visualize the space of expressions in 2D space by using the multidimensional scaling(MDS). But because there are too many facial expressions to select from, the user faces difficulty in navigating the space. So, we visualize the space hierarchically. To partition the space into a hierarchy of subspaces, we use fuzzy clustering. In the beginning, the system creates about 10 clusters from the space of 2400 facial expressions. Every tine the level increases, the system doubles the number of clusters. The cluster centers are displayed on 2D screen and are used as candidate key frames for key frame animation. The user selects new key frames along the navigation path of the previous level. At the maximum level, the user completes key frame specification. We let animators use the system to create example animations, and evaluate the system based on the results.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.