An autonomous and user-independent hand posture recognition system for vision-based interface tasks

Abstract

This paper presents a system for hand posture recognition that works with colour video streams under varying light conditions for human-machine interaction in vision-based interface tasks. No initialization of the system is required and no user dependence is involved. With this aim, we first model on-line each user's skin colour from the skin cue imaging of his/her face detected by means of Viola and Jones detector. Afterwards, a second order isomorphism approach performs tracking on skin colour blob based detected hand. Also, we propose this approach as a mechanism to estimate hand transition states. Finally, evidences about hand postures are recognized by shape matching, which is carried out through a holistic similarity measure focused on the Hausdorff distance. The paper includes experimental evaluations of the recognition system for 16 different hand postures in different video streams. The results show that the system can be suitable for real-time interfaces using general purpose hardware.