Flow Modeling and skin-based Gaussian pruning to recognize gestural actions using HMM

In this paper, we have proposed a novel approach to recognize the human hand/arm actions in the context of gesture recognition. The main idea is to model the flow information through mixture of Gaussians, perform skin-based Gaussian pruning, and to compute interlevel linking of non-pruned Gaussians using Kullback-Leibler (KL) divergence. Next, we have computed the temporal features from the matched Gaussians which are classified by Hidden Markov Model (HMM) to recognize the gestural action. The proposed approach is tested on six gestural actions taken in real situations and achieved 98% recognition results. Besides, we have performed a comparative analysis of different matching approaches where the KL divergence outperforms.