Gesture recognition using active body parts and active difference signatures

The introduction of low cost depth cameras along with advances in computer vision have spawned an exciting new era in Human Computer Interaction. Real time gesture recognition systems have become commonplace and attention has now turned towards making these systems invariant to within-user and user-to-user variation. Active difference signatures have been used to describe temporal motion as well as static difference from a canonical resting position. Geometric features, such as joint angles, and joint topological distances can be used along with active difference signatures as salient feature descriptors. To achieve robustness to natural gesture variation, this paper introduces active body part recognition along with these features into the Hidden Markov Model framework. The proposed method is bench-marked against other methods, achieving state of the art results on the MSR3D and ChaLearn datasets.