This paper describes 3-dimensional (3D) gesture recognition using Principal Component Analysis. Existing 2-dimensional gesture recognition systems have shortcomings such as limitation of motion. In order to solve this problem, motion recognition systems using 3D information were proposed. As vision-based 3D information has a number of dimensions as well as a lot of errors, however, it was difficult for those systems to find out consistent characteristics. In this paper, we describe a method of modeling and analyzing gestures using Principal Component Analysis. This method helps reduce the influences of errors that 3D data might have and achieve the effect of dimension reduction. We also propose a matching algorithm modified to reduce the motion limitation in the model-based motion recognition system, and present examples of using the result of motion recognition as the interface for 3D action games.
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