Most of the computer-vision-based hand gesture recognition systems are either confined to a fixed set of static gestures or only able to track 2D global hand motion. In order to recognize natural hand gestures such as those in American sign language, we need to track articulated hand motion in real time. The task is challenging due to the high degrees of freedom of the hand, self-occlusion, variable views, and lighting. This book focuses on automatic recovery of 3D hand motion from one or more views. The problem of hand tracking is formulated as Bayesian filtering in the framework of analysis-by-synthesis. We propose an Eigen Dynamic Analysis model and a new feature called likelihood edge. To automatically initialize and recover from loss-track, we proposed a bottom-up posture recognition algorithm. It collectively matches the local features in a single image with those in the image database. Through quantitative and visual experimental results, we demonstrate the effectiveness of our approach and point out its limitations.

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