The speaker will review the broad area of face recognition by computer, including problems such as face detection, face verification, and face identification. The perceptions about how well face recognition works vary widely. Dr. Learned-Miller will try to provide some background for understanding what's really going on, both in research and in industry, and what the state-of-the-art is. He will also discuss comparisons with human performance in face recognition.

In addition, Dr. Learned-Miller will review a variety of recent techniques for solving face recognition problems, some developed at UMass Amherst and some developed elsewhere, and give a sense of where current research is headed. Particularly interesting are the errors produced by current face recognition systems. He will conclude that the question of whether face recognition "works" is not meaningful without specifying the context in which it is done.

Erik G. Learned-Miller is an Associate Professor of Computer Science at the University of Massachusetts, Amherst, where he joined the faculty in 2004. He spent two years as a post-doctoral researcher at the University of California, Berkeley, in the Computer Science Division. Dr. Learned-Miller received a B.A. in Psychology from Yale University in 1988. In 1989, he co-founded CORITechs, Inc., where he co-developed the second FDA cleared system for image-guided neurosurgery. He worked for Nomos Corporation, Pittsburgh, PA, for two years as the manager of neurosurgical product engineering. He obtained his M.S. (1997) and Ph. D. (2002) degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. In 2006, he received an NSF CAREER award for his work in computer vision and machine learning. He is a Program Chair for the 2015 IEEE Conference on Computer Vision and Pattern Recognition.