A gesture-driven computer interface using Kinect

Automatic recognition of human actions from video has been studied for many years. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. However, the best-performing methods are complex and computationally-demanding and thus not well-suited for real-time deployments. This paper proposes to leverage the Kinect camera for close-range gesture recognition using two methods. Both methods use feature vectors that are derived from the skeleton model provided by the Kinect SDK in real-time. Although both methods perform nearest-neighbor classification, one method does this in the space of features using the Euclidean distance metric, while the other method does this in the space of feature covariances using a log-Euclidean metric. Both methods recognize 8 hand gestures in real time achieving correct-classification rates of over 99% on a dataset of 20 subjects but the method based on Euclidean distance requires feature-vector collections to be of the same size, is sensitive to temporal misalignment, and has higher computation and storage requirements.