VISUAL TRACKING AND RECOGNITION OF THE HUMAN HAND

Most of the computer-vision-based hand gesturerecognition systems are either confined to a fixedset of static gestures or only able to track 2Dglobal hand motion. In order to recognize natural hand gestures such asthose in American sign language, we need to trackarticulated hand motion in real time. The task ischallenging due to the high degrees of freedom of thehand, self-occlusion, variable views, and lighting. This book focuses on automatic recovery of 3D handmotion from one or more views.The problem of hand tracking is formulated asBayesian filtering in the framework ofanalysis-by-synthesis. We propose an Eigen DynamicAnalysis model and a new feature called likelihood edge. To automatically initialize and recover fromloss-track, we proposed a bottom-up posturerecognition algorithm. It collectively matches thelocal features in a single image with those in theimage database. Through quantitative and visualexperimental results, we demonstrate theeffectiveness of our approach and point out itslimitations.