Face localization in the neural abstraction pyramid

Abstract

One of the major parts in human-computer interface applications, such as face recognition and video-telephony, consists in the localization of a face in an image. I propose to use hierarchical neural networks with local recurrent connectivity to solve this task, even in presence of complex backgrounds, difficult lighting, and noise. The 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. Since the network processes an entire image, no time consuming scanning across positions and scales is needed. Its fast update allows for real-time face tracking.