Hand gesture recognition following the dynamics of a topology-preserving network

We present a new structure capable of characterizing hand posture, as well as its movement. Topology of a self-organizing neural network determines posture, whereas its adaptation dynamics throughout time determines gesture. This adaptive character of the network allows us to avoid the correspondence problem of other methods, so that the gestures are modelled by the movement of the neurons. To validate this method, we have trained the system with 12 gestures, some of which are very similar, and have obtained high success rates (over 97%). This application of a self-organizing network opens up a new field of research because its topology is used to characterize the objects and not to classify them, as is usually the case.