Real-Time hand Gesture Recognition Using Pseudo 3-D Hidden Markov Model

In the following work we present a new approach to recognition of hand gesture based on pseudo three-dimensional hidden Markov model (P3DHMM), a technique which can integrate spatial as well as temporal derived features in an elegant and efficient way. Additionally, robust and flexible hand gesture tracking using an appearance-based condensation tracker. These allow the recognition of dynamic gestures as well as more static gestures. Furthermore, there has been proposed to improve the overall performance of the approach: replace Baum-Welch algorithm with clustering algorithm, adding a clustering performance measure to the clustering algorithm and adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. Proposed improving methods along with the P3DHMM was used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a recognition rate of 99.1% in the gesture recognition experiments when compared to P2DHMMs