Learning face localization using hierarchical recurrent networks

Abstract

One of the major parts in human-computer interface applications, such as face recognition and video-telephony, consists in the exact localization of a face in an image. Here, we propose to use hierarchical neural networks with local recurrent connectivity to solve this task, even in presence of complex backgrounds, difficult lighting, and noise. Our network is trained using a database of gray-scale still images and manually determined eye coordinates. It is able to produce reliable and accurate eye coordinates for unknown images by iteratively refining an initial solution. The performance of the proposed approach is evaluated against a large test set. The fast network update allows for real-time operation. © Springer-Verlag Berlin Heidelberg 2002.