Abstract
Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible fingers. We explore kinematic constraints of the hand with forearm to extract finger geometric features which are translation, rotation and scale invariant. We first search hand components with the help of skeleton, and then order them into a serial arrangement according to either left hand or right hand and extract the geometric features among fingers, palm and forearm, finally those features are used in SVM classification for HPR. Our method can recognize twelve different types of hand postures for both hands respectively. Experiments under different illumination conditions and different scenes demonstrate the effectiveness and efficiency of the proposed method.