• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.97-104
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• 2009

This paper describes comparison and analysis of methodology which enables us in order to search the projection technique of optimum for projection in the plane. For this methodology, we applies the high-dimensional facial motion capture data respectively in linear and nonlinear projection techniques. The one core element of the methodology is to applies the high-dimensional facial expression data of frame unit in PCA where is a linear projection technique and Isomap, MDS, CCA, Sammon's Mapping and LLE where are a nonlinear projection techniques. And another is to find out the methodology which distributes in this low-dimensional space, and analyze the result last. For this goal, we calculate the distance between the high-dimensional facial expression frame data of existing. And we distribute it in two-dimensional plane space to maintain the distance relationship between the high-dimensional facial expression frame data of existing like that from the condition which applies linear and nonlinear projection techniques. When comparing the facial expression data which distribute in two-dimensional space and the data of existing, we find out the projection technique to maintain the relationship of distance between the frame data like that in condition of optimum. Finally, this paper compare linear and nonlinear projection techniques to projection high-dimensional facial expression data in low-dimensional space and analyze it. And we find out the projection technique of optimum from it.