SOMM: Self organizing Markov map for gesture recognition

Present work introduces a probabilistic recognition scheme for hand gestures. Self organizing feature maps are used to model spatiotemporal information extracted through image processing. Two models are built for each gesture category and, along with appropriate distance metrics, produce a validated classification mechanism that performs consistently during experiments on acted gestures video sequences. The main focus of current work is to tackle intra and inter user variability during gesture performance by adding flexibility to the decoding procedure and allowing the algorithm to perform an optimal trajectory search while the processing speed of both the feature extraction and the recognition process indicate that the proposed architecture is appropriate for real time and large scale lexicon applications.