The Biometric Consortium, supported by NIST and the NSA, exists to facilitate scientific and technical interchanges between the U.S. Federal government and outside entities on biometric and other identity technologies.
Over the last ten years or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding.
The BCOE is the FBI’s program for exploring and advancing the use of biometric technologies and capabilities for integration into operations. The BCOE strives to deliver state-of-the-art biometric tools and technologies to law enforcement and intelligence personnel around the world.
About Dr. WoodardDr. Woodard currently serves as the Director of AI Partnerships for the University of Florida’s Artificial Intelligence Initiative. He is currently an Associate Professor within the Electrical and Computer Engineering Department at the University of Florida where he is a member of the Florida Institute for Cybersecurity (FICS) Research. He is an IEEE Senior Member, an ACM Senior Member, a National Academy of Science Kavli Frontiers Fellow, and a member of the Association for the Advancement of Artificial Intelligence (AAAI). Dr. Woodard received his Ph.D. in Computer Science and Engineering from the University of Notre Dame, his M.E. in Computer Science and Engineering from Penn State University, and his B.S. in Computer Science and Computer Information Systems from Tulane University.
Before becoming a faculty member, Dr. Woodard was a Director of Central Intelligence postdoctoral fellow. His postdoctoral research focused on the development of advanced iris recognition systems using high-resolution sensors. His research interests include biometrics / identity science, artificial intelligence, applied machine learning, computer vision, and natural language processing. His current research projects include authorship attribution (stylometry) / computational behavioral analytics via text analytics and natural language processing, image analysis / machine learning based hardware assurance (hardware trojan detection, PCB bill of materials), and adversarial machine learning (DeepFake detection).