Extracting personal characteristics from human movement

We propose a new method for extracting personal characteristics from 3D body movement. We introduce the eigen action space to represent the personal characteristics. First, we estimate the average action from a set of 3D pose parameters from different people. Then we create the eigen action space from the covariance matrices of 3D pose parameters using the KL transform. Because the eigen action space consists of orthogonal base vectors, the 3D pose parameters of a person are represented as a point. A similarity measure is calculated from points in the action eigen space. Also, actions with new personal characteristics can be reconstructed by sampling new points in the eigen action space.