• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.18-26
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• 2008

This paper presents a phased visualization method of facial expression space that enables the user to control facial expression of 3D avatars by select a sequence of facial frames from the facial expression space. Our system based on this method creates the 2D facial expression space from approximately 2400 facial expression frames, which is the set of neutral expression and 11 motions. The facial expression control of 3D avatars is carried out in realtime when users navigate through facial expression space. But because facial expression space can phased expression control from radical expressions to detail expressions. So this system need phased visualization method. To phased visualization the facial expression space, this paper use fuzzy clustering. In the beginning, the system creates 11 clusters from the space of 2400 facial expressions. Every time the level of phase increases, the system doubles the number of clusters. At this time, the positions of cluster center and expression of the expression space were not equal. So, we fix the shortest expression from cluster center for cluster center. We let users use the system to control phased facial expression of 3D avatar, and evaluate the system based on the results.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.85-90
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• 2007

This paper presents a facial animation and expression control method that enables the animator to select any facial frames from the facial expression space, whose expression transfer paths the system can setup automatically. Our system creates the facial expression space from approximately 2500 captured facial frames. To create the facial expression space, we get distance between pairs of feature points on the face and visualize the space of expressions in 2D space by using the Multidimensional scaling(MDS). To setup most suitable expression transfer paths, we classify the facial expression space into four field on the basis of any facial expression state. And the system determine the state of expression in the shortest distance from every field, then the system transfer from the state of any expression to the nearest state of expression among thats. To complete setup, our system continue transfer by find second, third, or fourth near state of expression until finish. If the animator selects any key frames from facial expression space, our system setup expression transfer paths automatically. We let animators use the system to create example animations or to control facial expression, and evaluate the system based on the results.

• 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.

• 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 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.

• 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.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.569-578
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• 2003

This paper presents a method in which the user produces a real-time facial animation by navigating in the space of facial expressions created from a great number of captured facial expressions. The core of the method is define the distance between each facial expressions and how to distribute into suitable intuitive space using it and user interface to generate realtime facial expression animation in this space. We created the search space from about 2,400 raptured facial expression frames. And, when the user free travels through the space, facial expressions located on the path are displayed in sequence. To visually distribute about 2,400 captured racial expressions in the space, we need to calculate distance between each frames. And we use Floyd's algorithm to get all-pairs shortest path between each frames, then get the manifold distance using it. The distribution of frames in intuitive space apply a multi-dimensional scaling using manifold distance of facial expression frames, and distributed in 2D space. We distributed into intuitive space with keep distance between facial expression frames in the original form. So, The method presented at this paper has large advantage that free navigate and not limited into intuitive space to generate facial expression animation because of always existing the facial expression frames to navigate by user. Also, It is very efficient that confirm and regenerate nth realtime generation using user interface easy to use for facial expression animation user want.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.277-284
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• 2004

This paper presents a facial expression control method of 3D avatar 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 spare from about 2,400 captured facial frames. 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 11 clusters from the space of 2,400 facial expressions. The cluster centers are displayed on 2D screen and are used as candidate key frames for key frame animation. When the user zooms in (zoom is discrete), it means that the user wants to see mort details. So, the system creates more clusters for the new level of zoom-in. Every time the level of zoom-in increases, the system doubles the number of clusters. The user selects new key frames along the navigation path of the previous level. At the maximum zoom-in, the user completes facial expression control specification. At the maximum, the user can go back to previous level by zooming out, and update the navigation path. We let users use the system to control facial expression of 3D avatar, and evaluate the system based on the results.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.309-316
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• 2010

This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.

• 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.