Collaborative Face Orientation Detection in Wireless Image Sensor Networks

Most face recognition and tracking techniques employed in surveillance and human-computer interaction (HCI) systems rely on the assumption of a frontal view of the human face. In alternative approaches, knowledge of the orientation angle of the face in captured images can improve the performance of techniques based on non-frontal face views. In this paper, we propose a collaborative technique for face analysis in smart camera networks with a dual objective of detecting the camera view closest to a frontal view of the subject, and estimating the face orientation angles in all the camera views based on additional fusion of local angle estimates. Soft information indicating the probabilities of face and eye candidates in each image is exchanged between the cameras, and epipolar geometry mapping is employed to assess correspondence between candidates in different views. Once the camera with the closest view to the frontal face view is identified, further exchange of the face orientation angles estimated by all cameras allows for a collaborative refinement of the estimates according to their associated confidence levels. The proposed collaborative detection and estimation schemes employ low-complexity algorithms and do not require image transfer between the cameras. Hence, these schemes are applicable to networks of image sensors with in-node processing and narrowband wireless communication.