Dynamic recognition of gestures from video sequences is a challenging task due to the high variability in the characteristics of each gesture with respect to different individuals. In this work, we propose a novel representation of gestures as linear combinations of the elements of an overcomplete dictionary, based on the emerging theory of sparse representations. We evaluate our approach on a publicly available gesture dataset of Palm Grafti Digits and compare it with other state-of-the-art methods, such as Hidden Markov Models, Dynamic Time Warping and the recently proposed distance metric termed Move-Split-Merge. Our experimental results suggest that the proposed recognition scheme offers high recognition accuracy in isolated gesture recognition and a satisfying robustness to noisy data, thus indicating that sparse representations can be successfully applied in the field of gesture recognition.
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