Rule-based hand posture recognition using qualitative finger configurations acquired with the Kinect

Gesture recognition systems exhibit failures when faced with large hand posture vocabularies or relatively new hand poses. One main reason is that 2D and 3D appearance-based approaches require significant amounts of training data. We address this problem by introducing a new 2D model-based approach to recognize hand postures. The model captures a high-level rule-based representation of the hand expressed in terms of finger poses and their qualitative configuration. The available 3D information is used to first segment the hand. We evaluate our approach on a Kinect dataset and report superior results while using less training data when comparing to state-of-the-art 3D SURF descriptor.

Jaar van publicatie:2013