Hand gesture recognition using a real-time tracking method and hidden Markov models

摘要

In this paper, we introduce a hand gesture recognition system to recognize continuous gesture before stationary background. The system consists of four modules: a real time hand tracking and extraction, feature extraction, hidden Markov model (HMM) training, and gesture recognition. First, we apply a real-time hand tracking and extraction algorithm to trace the moving hand and extract the hand region, then we use the Fourier descriptor (FD) to characterize spatial features and the motion analysis to characterize the temporal features. We combine the spatial and temporal features of the input image sequence as our feature vector. After having extracted the feature vectors, we apply HMMs to recognize the input gesture. The gesture to be recognized is separately scored against different HMMs. The model with the highest score indicates the corresponding gesture. In the experiments, we have tested our system to recognize 20 different gestures, and the recognizing rate is above 90%.