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Keynote Lectures

Biases, Discrimination, and Fairness in Biometrics and Beyond
Julian Fierrez, Universidad Autonoma de Madrid, Spain

3D Motion Analysis with Event-based Sensors
Cornelia Fermüller, University of Maryland, United States

To be announced soon.
Marco Gori, University of Siena, Italy

 

Biases, Discrimination, and Fairness in Biometrics and Beyond

Julian Fierrez
Universidad Autonoma de Madrid
Spain
 

Brief Bio
Julian Fierrez received the M.Sc. and Ph.D. degrees in telecommunications engineering from Universidad Politecnica de Madrid, Spain, in 2001 and 2006, respectively. Since 2004 he has been at Universidad Autonoma de Madrid, where he is currently an Associate Professor. His research interests include signal and image processing, pattern recognition, and biometrics; with emphasis on evaluation, security, forensics, mobile, and behavioral biometrics. He is actively involved in EU projects around biometrics (e.g., BIOSECURE, TABULA RASA, and BEAT in the past; now IDEA-FAST, PRIMA, and TRESPASS-ETN). He has received multiple best paper awards in key conferences around biometrics, and multiple world-class research distinctions, including: 2006 EBF European Biometrics Industry Award, EURASIP Best PhD Award 2012, Miguel Catalan Award 2015 to the Best Researcher under 40 in the Community of Madrid in the general area of science and technology, and the 2017 IAPR Young Biometrics Investigator Award, given to a single researcher worldwide every two years under the age of 40, whose research work has had a major impact in biometrics. He is now Associate Editor of the IEEE Trans. on Information Forensics and Security, the IEEE Trans. on Image Processing, and Elsevier Information Fusion. [http://biometrics.eps.uam.es/fierrez]


Abstract
Available soon.



 

 

3D Motion Analysis with Event-based Sensors

Cornelia Fermüller
University of Maryland
United States
 

Brief Bio
Cornelia Fermüller is a research scientist at the Institute for Advanced Computer Studies (UMIACS) at the University of Maryland at College Park.  She holds a Ph.D. from the Technical University of Vienna, Austria and an M.S. from the University of Technology, Graz, Austria, both in Applied Mathematics.  She co-founded the Autonomy Cognition and Robotics (ARC) Lab and co-leads the Perception and Robotics Group at UMD. She is the PI of an NSF-sponsored Science of Learning Center Network for Neuromorphic Engineering. Her research is in the areas of Computer Vision, Human Vision, and Robotics. She studies and develops biologically inspired Computer Vision solutions for systems that interact with their environment. In recent years, her work has focused on the interpretation of human activities, and on motion processing for fast active robots (such as drones) using as input bio-inspired event-based sensors.

http://users.umiacs.umd.edu/users/fer


Abstract
Event-based sensors have gained increased popularity in the fields of Computer Vision and Robotics because they offer exciting alternatives for motion perception. These neuromorphic imaging devices, inspired by the transient pathway of mammalian vision, record at very high temporal resolution the changes in the scene. In this way, they produce a continuous stream of events with each pixel operating asynchronously and independently, which allows us to interpret continuous motion. The data’s unique properties (high dynamic range and temporal resolution, low latency and bandwidth) are valuable for solving the tasks of the early visual motion pathway - 3D motion estimation, segmentation and tracking robustly in even challenging scenarios.

Treating the data as point clouds - the so-called event clouds in x-y-t space, we developed new constraints, developed algorithms, and demonstrated them in robotics applications. The constraints relate the event clouds and their surface normal vectors to 3D motion, and the shape of the clouds to scene geometry and object motions. These constraints are used by aligning clouds using time and space information, and implemented in classical optimizations as well as deep neural networks. Finally, new datasets for evaluating 3D motion, structure, and depth were collected, and autonomous driving as well as drone applications for tracking, dodging and pursuit were developed.



 

 

To be announced soon.

Marco Gori
University of Siena
Italy
 

Brief Bio
Available soon.


Abstract
Available soon.



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