Prof. Ioannis A. Kakadiaris. PhD – University of Houston
Biography: Professor Ioannis A. Kakadiaris, Ph.D., serves as the Director of the Borders, Trade, and Immigration Institute, a Department of Homeland Security Center of Excellence led by the University of Houston (UH). As director for BTI Institute, Ioannis oversees multiple projects, undertaken with seventeen partners across nine states, which provide homeland security enterprise education and workforce development and which study complex, multi-disciplinary issues related to flows of people, goods, and data across borders. A Hugh Roy and Lillie Cranz Cullen Distinguished University Professor of Computer Science, Ioannis is also an international expert in facial recognition and data/video analytics. He earned his B.S. in physics at the University of Athens in Greece, his M.S. in computer science from Northeastern University, and his Ph.D. in computer science at the University of Pennsylvania. In addition to twice winning the UH Computer Science Research Excellence Award, Ioannis has been recognized for his work with several distinguished honors, including the NSF Early Career Development Award, the Schlumberger Technical Foundation Award, the UH Enron Teaching Excellence Award, and the James Muller Vulnerable Plaque Young Investigator Prize..
Keynote Title: Face Recognition: Recent Progress and Opportunities
Abstract: In the first part of the talk, I will present the Borders, Trade and Immigration institute. The mission is to monitor and facilitate transnational flows of people and goods, lead transformational technology-driven solutions and data-informed policies, as well as provide enterprise education and workforce development for homeland security. One of the major technologies for security and facilitation leading to social, cultural and economic development is Biometrics. In the second part, I will present an overview of the Face Recognition Software system pipeline developed and maintained at the UH Computational Biomedicine Lab which has achieved state-of-the-art performance on a challenging database to be introduced at B-Wild 2017.
Ass. Prof. Walter J. Scheirer, PhD – University of Notre Dame
Biography: Walter J. Scheirer, Ph.D. is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Previously, he was a postdoctoral fellow at Harvard University, with affiliations in the School of Engineering and Applied Sciences, Dept. of Molecular and Cellular Biology and Center for Brain Science, and the director of research & development at Securics, Inc., an early stage company producing innovative computer vision-based solutions. He received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. Dr. Scheirer has extensive experience in the areas of computer vision and human biometrics, with an emphasis on advanced learning techniques. His overarching research interest is the fundamental problem of recognition, including the representations and algorithms supporting solutions to it. Website: http://www.wjscheirer.com/
Keynote Title: Visual Psychophysics for Facial Analysis
Abstract: For many problems in computer vision, human learners are considerably better than machines. Humans possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. In this talk, an advanced online psychometric testing platform will be described that makes new kinds of annotation data available for learning. Subsequently, a new technique for harnessing these new kinds of information – “perceptual annotations” – for support vector machines, support vector regression, and convolutional neural networks will be introduced. A key intuition for this approach is that while it may remain infeasible to dramatically increase the amount of data and high-quality labels available for the training of a given system, measuring the exemplar-by-exemplar difficulty and pattern of errors of human annotators can provide important information for regularizing the solution of the system at hand.