Body gestures play an important role in human communications, specially hand gestures are the most distinctive features in sign languages. Several works have been proposed in order to recognize hand gestures using static and dynamic approaches. Nevertheless, due to the high variety of signs and the dynamic changes exhibited in different hand motions, a strategy for modeling these dynamic changes in hand signs must be fulfilled. In this work we propose a framework for dynamic hand gesture recognition using a well known method for alignment of time series as the Generalized Time Warping (GTW). Several features are extracted from the aligned sequences of hand gestures based on texture descriptors. Then a methodology for hand motion recognition is carried out based on Convolutional Neural Networks. The obtained results show that the methodology proposed allows an accurate recognition of several hand gestures obtained from the RVL-SLLL American Sign Language Database.
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