DTW-based gait recognition from recovered 3-D joint angles and inter-ankle distance

We present a view independent approach for 3D human gait recognition. The identification of the person is done on the basis of motion estimated by our marker-less 3D motion tracking algorithm. We show tracking performance using ground-truth data acquired by Vicon motion capture system. The identification is achieved by dynamic time warping using both joint angles and inter-joint distances. We show how to calculate approximate Euclidean distance metric between two sets of Euler angles. We compare the correctly classified ratio obtained by DTW built on unit quaternion distance metric and such an Euler angle distance metric. We then show that combining the rotation distances with inter-ankle distances and other person attributes like height leads to considerably better correctly classified ratio.