Gaze directed camera control for face image acquisition

Face recognition in surveillance situations usually requires high resolution face images to be captured from remote active cameras. Since the recognition accuracy is typically a function of the face direction with frontal faces more likely to lead to reliable recognition we propose a system which optimises the capturing of such images by using coarse gaze estimates from a static camera. By considering the potential information gain from observing each target, our system automatically sets the pan, tilt and zoom values (i.e. the field of view) of multiple cameras observing different tracked targets in order to maximise the likelihood of correct identification. The expected gain in information is influenced by the controllable field of view, and by the false positive and negative rates of the identification process, which are in turn a function of the gaze angle. We validate the approach using a combination of simulated situations and real tracking output to demonstrate superior performance over alternative approaches, notably using no gaze information, or using gaze inferred from direction of travel (i.e. assuming each person is always looking directly ahead).We also show results from a live implementation with a static camera and two pan-tilt-zoom devices, involving real-time tracking, processing and control.