Faculty Host: Vladimir Pavlovic

Abstract:

Face detection is a canonical example of a rare event detection problem, in which target patterns occur with much lower frequency than non-targets. Out of millions of face-sized windows in an input image, for example, only a few will typically contain a face. Viola and Jones recently proposed a cascade architecture for face detection which successfully addresses the rare event nature of the task. A ceytral part of their method is a feature selection algorithm based on AdaBoost. We presnnt a novel cascade learning algorithm based on forward feature selection which is two orders of magnitude faster than the Viola-Jones approach and yields classifiers of similar quality. This faster method could be used for more demanding classification tasks, such as on-line learning or searching the space of classifier structures. In addition, we describe an improved training criterion, based on the idea of an indifference curve, and a perturbation bias technique which results in better classification performance. technique which results in better classification performance.

This is joint work with Jianxin Wu, Matthew D. Mullin, Jie Sun and Aaron Bobick

Speaker Bio:

James M. Rehg received his Ph.D. from Carnegie Mellon University in 1995. From 1996 to 2001 he led the computer vision research group at the Cambridge Research Laboratory of the Digital Equipment Corporation, which was acquired by Compaq Computer Corporation in 1998. In 2001, he joined the faculty of the College of Computing at the Georgia Institute of Technology, where he is currently an Associate Professor. His research interests include computer vision, machine learning, human-computer interaction, computer graphics, and distributed computing.