Keynote Lectures

Face De-identification for Privacy Protection
Slobodan Ribarić, University of Zagreb, Croatia

Remote Monitoring of Neurodegeneration through Speech
Elmar Nöth, Friedrich-Alexander-Universitaet Erlangen-Nuernberg, Germany

Privacy Protection in Visual Data
Bernhard Rinner, Alpen-Adria-Universität Klagenfurt, Austria


Face De-identification for Privacy Protection

Slobodan Ribarić
University of Zagreb
Croatia





Brief Bio
Slobodan Ribarić, Ph.D., is a Full Professor at the Department of Electronics, Microelectronics, Computer and Intelligent Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia. S. Ribarić is a head of the Laboratory of Pattern Recognition and Biometric Security Systems (RUBOISS). Slobodan Ribarić received the B.S. degree in electronics, the M.S. degree in automatics, and the PhD. degree in electrical engineering from the Faculty of Electrical Engineering, Ljubljana, Slovenia, in 1974, 1976, and 1982, respectively. His research interests include Pattern Recognition, Artificial Intelligence, Biometrics, Computer Architecture and Robot Vision. He has published more than one hundred and fifty papers on these topics. Ribarić is author of five books and co-author of the book An Introduction to Pattern Recognition. Professor Ribarić has held a series of invited lectures at universities and institutes in China, Germany, Italy, India, Denmark and Slovenia. He is a member of Editorial Board of Journal of Computing and Information Technology (CIT). Ribarić is a member of the IEEE and MIPRO.

Abstract
Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services such as communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Privacy described as "an integral part of our humanity" and "the beginning of all freedom", has no unique definition; even more, it is a concept in disarray. De-identification is one of the basic methods for protecting privacy. It is defined as the process of removing or concealing personal identifiable information, or replacing them with surrogate personal identifiers, in order to prevent the recognition (identification) of a person directly or indirectly, for example, via association with an identifier, user agent, or device. In general, a person can be identified on the basis of biometric personal identifiers, but also by combination of different types of biometric personal identifiers and non-biometric personal identifiers, such as environmental and/or specific socio-political context, speech context, and dressing style. The main physiological biometric identifier, which can be collected at a distance and use for identification, requiring de-identification for privacy preservation is face. The early research into face de-identification was focused on face still images, and recommended the use of ad-hoc or so-called naive approaches such as "black box", “blurring” and “pixelation” of the image region occupied by the face. To achieve an improved level of privacy protection, more sophisticated approaches have been proposed: eigenvector-based de-identification method, k-Same-Select, Model-based k-Same, morphing-based methods and scrambling. Special attention is devoted to automatic face de-identification in video surveillance systems, as well as drone-based surveillance systems, due to tremendous development and use of visual technologies such as CCTVs, visual sensor networks, camera phones and drones. A survey of approaches, methods and solutions for face de-identification in still images, wild scenes and videos are presented.


Remote Monitoring of Neurodegeneration through Speech

Elmar Nöth
Friedrich-Alexander-Universitaet Erlangen-Nuernberg
Germany







Brief Bio
Elmar Nöth is a professor for Applied Computer Science at the University of Erlangen-Nuremberg. He studied in Erlangen and at M.I.T. and received the Dipl.-Inf. and the Dr.-Ing. degree from the University of Erlangen-Nuremberg in 1985 and 1990, respectively. Since 1990 he was an assistant professor at the Institute for Pattern Recognition in Erlangen. Since 2008 he is a full professor at the same institute and head of the speech group. He is one of the founders of the Sympalog Company, which markets conversational dialogue systems. He is author or co-author of more than 350 articles. His current interests are prosody, analysis of pathologic speech, computer aided language learning and emotion analysis.

Abstract
In this talk we will report on the results of the workshop on Remote Monitoring of Neurodegeneration through Speech, which was part of the "Third Frederick Jelinek Memorial Summer Workshop" and took place at Johns Hopkins University in Baltimore, USA from June 13th to August 5th, 2016. We will concentrate on Colombian-Spanish multi-modal data from people with Parkinson's disease that contain speech, gait, and hand-writing data.


Privacy Protection in Visual Data

Bernhard Rinner
Alpen-Adria-Universität Klagenfurt
Austria









Brief Bio
Bernhard Rinner is professor at the Alpen-Adria-Universität Klagenfurt, Austria where he is heading the Pervasive Computing group. He is deputy head of the Institute of Networked and Embedded Systems and served as vice dean of the Faculty of Technical Sciences from 2008-2011. Before joining Klagenfurt he was with Graz University of Technology and held research positions at the Department of Computer Sciences at the University of Texas at Austin in 1995 and 1998/99. His current research interests include embedded computing, sensor networks and pervasive computing. Bernhard Rinner has been co-founder and general chair of the ACM/IEEE International Conference on Distributed Smart Cameras and has served as chief editor of a special issue on this topic in The Proceedings of the IEEE. Currently, he is Associate Editor for Ad Hoc Networks Journal and EURASIP Journal on Embedded Systems. Together with partners from four European universities, he has jointly initiated the Erasmus Mundus Joint Doctorate Program on Interactive and Cognitive Environments (ICE). He is member of IEEE and IFIP and member of the board of the Austrian Science Fund.

Abstract
Installed on public places, integrated in cellphones or deployed in the Internet of Things – cameras have become ubiquitous, and they capture highly sensitive information about our everyday life. Advanced analytics and steadily increasing networking pose soaring risks to our privacy. In this talk I will give an overview of privacy challenges in visual data. I will then present selected key achievements from our ten years’ research in privacy-aware cameras. A key approach for privacy protection is to deteriorate the image quality of selected areas or entire frames. We have developed “cartooning” as an onboard protection method which requires low computational resources and keeps the utility of the protected video high. In another research thread we have investigated adaptive privacy filters which modify the strength of the deterioration based on the captured scenes. I will conclude the talk by demonstrating our TrustEYE, an embedded smart camera which performs onboard privacy filtering and secures all delivered data with dedicated hardware.