  Many of the state-of-the-art algorithms for gesture recognition are based on
Conditional Random Fields (CRFs). Successful approaches, such as the
Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose
values are mapped to the values of the labels. In this paper we propose a novel
methodology to set the latent values according to the gesture complexity. We
use an heuristic that iterates through the samples associated with each label
value, stimating their complexity. We then use it to assign the latent values
to the label values. We evaluate our method on the task of recognizing human
gestures from video streams. The experiments were performed in binary datasets,
generated by grouping different labels. Our results demonstrate that our
approach outperforms the arbitrary one in many cases, increasing the accuracy
by up to 10%.
