Active gesture recognition using partially observable Markov decision processes

We present a foveated gesture recognition system that guides an active camera to foveate salient features based on a reinforcement learning paradigm. Using vision routines previously implemented for an interactive environment, we determine the spatial location of salient body parts of a user and guide an active camera to obtain images of gestures of expressions. A hidden-state reinforcement learning paradigm based on the partially observable Markov decision process (POMDP) is used to implement this visual attention. The attention module selects targets to foveate based on the goal of successful recognition, and uses a new multiple-model Q-learning formulation. Given a set of target and distracter gestures, our system can learn where to foveate to maximally discriminate a particular gesture