Marina L. Gavrilova
University of Calgary, Canada
Prof. Gavrilova holds Full Professor with Tenure appointment at the Department of Computer Science, University of Calgary, Canada. Prof. Gavrilova research interests lie in the areas of machine intelligence, biometric recognition, image processing and GIS. Prof. Gavrilova publication list includes over 300 journal and conference papers, edited special issues, books and book chapters, including World Scientific Bestseller of the Month (2007) – “Image Pattern Recognition: Synthesis and Analysis in Biometric,” Springer book (2009) “Computational Intelligence: A Geometry-Based Approach” and IGI book (2013) “Multimodal Biometrics and Intelligent Image Processing for Security Systems”. She has received support from CFI, NSERC, GEOIDE, MITACS, PIMS, Alberta Ingenuity, NATO and other funding agencies. She is an Editor-in-Chief of Transactions on Computational Sciences Springer Verlag Journal series and on Editorial board of seven journals. Prof. Gavrilova received numerous awards and her research was profiled in newspaper and TV interviews, most recently being chosen together with other five outstanding Canadian scientists to be featured in National Museum of Civilization, National Film Canada production, and on Discovery Channel Canada.
Keynote Title: A New Frontier: Deep Machine Learning for Biometric Privacy and Security
Abstract: Current scientific discourse identifies human identity recognition as one of the crucial tasks performed by government, social services, consumer, financial and health institutions worldwide. Biometric image and signal processing is increasingly used in a variety of applications to mitigate vulnerabilities, to predict risks, and to allow for rich and more intelligent data analytics. But there is an inherent conflict between enforcing stronger security and ensuring privacy rights protection. This keynote lecture looks at the new horizons that are currently being explored through integration of deep learning techniques with computer vision and biometric security research. It discusses how multi-modal biometric systems can benefit from the integration of advanced machine learning methods based on both supervised (SVM, KNN, DTrees) and deep learning (NN, CNN, SNN) approaches for image and signal processing. It also describes the developed prototype systems that can extracts and analyze not only traditional, but also emerging social behavioral patterns, such as spatial, temporal, contextual, linguistic, relational and even aesthetic data. Finally, it touches on challenges that uncontrolled data mining and sharing present to privacy and suggests some ways to mitigate them.