View invariant gesture recognition using 3D motion primitives

This paper presents a method for automatic recognition of human gestures. The method works with 3D image data from a range camera to achieve invariance to viewpoint. The recognition is based solely on motion from characteristic instances of the gestures. These instances are denoted 3D motion primitives. The method extracts 3D motion from range images and represent the motion from each input frame in a view invariant manner using harmonic shape context. The harmonic shape context is classified as a 3D motion primitive. A sequence of input frames results in a set of primitives that are classified as a gesture using a probabilistic edit distance method. The system has been trained on frontal images (0deg camera rotation) and tested on 240 video sequences from 0deg and 45deg. An overall recognition rate of 82.9% is achieved. The recognition rate is independent of the viewpoint which shows that the method is indeed view invariant.