This paper introduces a framework for gesture and action recognition based on the
evolution of temporal gesture primitives, or subgestures. Our work is inspired on the
principle of producing genetic variations within a population of gesture subsequences,
with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation
of actions and gestures outperforms the state of the art results on the MSRDaily3D and
MSRAction3D datasets
