Natural hand posture recognition based on Zernike moments and hierarchical classifier

View-independence and user-independence are two fundamental requirements for hand posture recognition during natural human-robot interaction. However only a few research concerns on the two issues simultaneously. The difficulty for natural gesture-based human-robot interaction lies in that appearances of the same hand posture vary with different users from different viewing directions. In this paper, we propose a systematic feature selection approach based on Zernike moments and Isomap dimensionality reduction. A hierarchical classifier based on multivariate decision tree and piecewise linearization is developed to deal with the irregular distribution of the same hand postures. The proposed method is compared with other commonly used ones in hand posture recognition. Experimental results indicate that the proposed method can effectively identify different hand postures, irrespective of viewing directions and users.