Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking

Nonverbal communication forms a substantial portion of human–human interaction. In recent years, there has been increasing interest in developing gesture-based user interfaces for better human–machine interaction. Hand and face tracking is a central issue in the development of real-time gesture recognition systems. In this article, a new approach for boundary detection in blob tracking based on the mean-shift algorithm is proposed. Our approach is based on continuous sampling of the boundaries of the kernel and changing the size of the kernel using our novel Fuzzy-based algorithm. We compare our approach to the kernel density-based approach which is known as the CAM-shift algorithm in a set of different noise levels and conditions. The results show that the proposed approach is superior in stability against white noise and also provides correct boundary detection for arbitrary hand postures which is not achievable by the CAM-shift algorithm. This algorithm provides the required framework for vision-based real-time gesture recognition and hand and face tracking. It can be applied in scientific and commercial extensions of either vision-based or hybrid gesture recognition systems.