Learning symmetric face pose models online using locally weighted projectron regression

Human localization is fundamental in human centered computing and human-robot interaction (HRI), as human operators should be localized by robots before being actively serviced. This paper proposes a simple and efficient approach for estimating the distance and orientation of an human, from a single robot-acquired image. We adopt a simple combination of multiple Haar feature-based classifiers to compute face scores, that represent the probability that the detected face is acquired from each of a predefined set of poses. Using the Locally Weighted Projectron Regression (LWPR), an online incremental regression-based learning scheme, we can reliably learn and predict the pose of a human face in real-time at a low computational cost. The accuracy, robustness, and scalability of the obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.