Face localization and tracking in the neural abstraction pyramid

This paper proposes to use hierarchical neural networks with local recurrent connectivity to solve the task of localizing a human face in an image not only in unambiguous situations, but also in the presence of complex backgrounds, difficult lighting, and noise.

Abstract

One of the major tasks in some human–computer interface applications, such as face recognition and video telephony, is to localize a human face in an image. In this paper, we propose to use hierarchical neural networks with local recurrent connectivity to solve this task not only in unambiguous situations, but also in the presence of complex backgrounds, difficult lighting, and noise. The networks are trained using a database of gray-scale still images and manually determined eye coordinates. They are able to produce reliable and accurate eye coordinates for unknown images by iteratively refining initial solutions. Because the networks process entire images, there is no need for any time-consuming scanning across positions and scales. Furthermore, the fast network updates allow for real-time face tracking. In this case, the networks are trained using still images that move in random directions. The trained networks are able to accurately track the eye positions in the test image sequences.