A Comparative Study on Using Zernike Velocity Moments and Hidden Markov Models for Hand Gesture Recognition

Hand-gesture recognition presents a challenging problem for computer vision due to the articulated structure of the human hand and the complexity of the environments in which it is typically applied. Solving such a problem requires a robust tracking mechanism which in turn depends on an effective feature descriptor and classifier. Moment invariants, as discriminative feature descriptors, have been used for shape representation for many years. Zernike moments have been particularly attractive for their scale, translation and rotation invariance. More recently, Zernike moments have been extended to a spatio-temporal descriptor, known as the Zernike velocity moment, through combining with the displacement vector of the centre of mass of the target object between video frames. This descriptor has hitherto been demonstrated successfully in human gait analysis. In this paper, we introduce and evaluate the application of Zernike velocity moments in hand-gesture recognition, and compare with a bank of hidden Markov models with Zernike moments as observations. We demonstrate good performance for both approaches, with a substantial increase in performance for the latter method.