  Defining methods for the automatic understanding of gestures is of paramount
importance in many application contexts and in Virtual Reality applications for
creating more natural and easy-to-use human-computer interaction methods. In
this paper, we present a method for the recognition of a set of non-static
gestures acquired through the Leap Motion sensor. The acquired gesture
information is converted in color images, where the variation of hand joint
positions during the gesture are projected on a plane and temporal information
is represented with color intensity of the projected points. The classification
of the gestures is performed using a deep Convolutional Neural Network (CNN). A
modified version of the popular ResNet-50 architecture is adopted, obtained by
removing the last fully connected layer and adding a new layer with as many
neurons as the considered gesture classes. The method has been successfully
applied to the existing reference dataset and preliminary tests have already
been performed for the real-time recognition of dynamic gestures performed by
users.
