Detecting and tracking human face and eye using an space-varying sensor and an active vision head

We have developed a system for detecting and tracking human face and eye in an unstructured environment. We adopt a biologically plausible retinally connected neural network architecture and integrate it with an active vision system. While the active vision system tracks the object moving in real time, the neural network detects the face and eye location from the video stream at a slower rate. The paper provides a systematic way of creating and selecting examples for training the network by exploring the link between theory and practice. Experimental results on a real sequence of images from a space-varying sensor depicts the performance of the system.