Application of Pseudo 2-D Hidden Markov Model for Hand Gesture Recognition

Hand gesture recognition is the temporal pattern analysis with mathematical interpretation. It provides the means for the non-verbal communication among the people, more natural and powerful means of human-computer interaction (HCI) for the virtual reality application. The development of human-computer stochastic processes has led to a 1-D hidden Markov models (1DHMMs) and training algorithms to find the high recognition rate and low computational complexity. Due to their dimensionality and computational efficiency, Pseudo 2-D HMMs (P2DHMMs) are often favored for a flexible way of presenting events with temporal and dynamic variations. Both 1-D HMM and 2-D HMM are present in hand gestures, which are of increasing interest in the research of hand gesture recognition (HGR). The main issue of 1-D HMM is the fact that the recursiveness in the forward and backward procedures typically multiply probability values between themselves. Hence, this product quickly tends to zero and goes beyond any machine storage capabilities. This work presents an application of Pseudo 2-D HMM to classify the hand gestures from measured values of an accelerating image. Comparing an experimental result between 1-D HMM and Pseudo 2-D HMM with respect to recognition rate and accuracy, it shows a prominent result for the proposed approach.