Keynote Speaker I
Prof. Patrick Bours
Norwegian University of Science and Technology, Norway
Patrick Bours recieved his MSc and PhD degree in the area of Discrete Mathematics from the Eindhoven University of Technology in the Netherlands, in 1990 and 1994. From 1995 until June 2005 he worked as a senior policy member in the area of cryptology for the Netherlands National Communication Security Agency (NLNCSA). From July 2005 he worked at the Gjøvik University College in Gjøvik, Norway. First as a Postdoc (2005-2008), then as an associate professor (2008-2012) and since 2012 he holds a professor position. Gjøvik University College merged with NTNU in 2016. Since 2005 he is working in the area of biometrics, and in particular behavioural biometrics. He has over 100 publications in the area of gait recognition, keystroke and mouse dynamics, as well as ear, fingerprint, face and retina recognition. His current research interest is in the area of keystroke dynamics, in particular continuous authentication and application of keystroke dynamics. Patrick Bours is reviewer for various conferences and journals in the area of biometrics and he is an associate editor for Wiley's journal on Security and Privacy (SPY).
Speech Title: "Using Behavioural Biometrics Beyond Gaining Access"
Abstract: Biometrics is traditionally used to gain access to a system. Fingerprint and face are used to gain access to our phones, iris scans are used at boarder control to gain access to a restricted area and finger vein biometrics is applied to withdraw money from ATM machines. Behavioural biometrics in particular can be used far beyond such applications, mainly because we can measure the behaviour of a person unobtrusively over longer periods of time. In particular will I show how behavioural biometrics can be used to add an additional layer of security to your computer or mobile device and how we can use it to provide safety online for vulnerable members of society.
Keynote Speaker II
Prof. Guoying Zhao
University of Oulu, Finland
Guoying Zhao received the Ph.D. degree in computer science from the Chinese Academy of Sciences, Beijing, China, in 2005. She is currently a Professor with the Center for Machine Vision and Signal Analysis, University of Oulu, Finland, where she worked as a senior researcher since 2005 and an Associate Professor since 2014. She has authored or co-authored more than 190 papers in journals and conferences. Her papers have currently over 9300 citations in Google Scholar (h-index 43). She was co-publicity chair for FG2018, has served as area chairs for several conferences and is associate editor for Pattern Recognition, IEEE Transactions on Circuits and Systems for Video Technology, and Image and Vision Computing Journals. She has lectured tutorials at ICPR 2006, ICCV 2009, SCIA 2013 and FG 2018, authored/edited three books and eight special issues in journals. Dr. Zhao was a Co-Chair of many International Workshops at ECCV, ICCV, CVPR, ACCV and BMVC. Her current research interests include image and video descriptors, facial-expression and micro-expression recognition, gait analysis, dynamic-texture recognition, human motion analysis, and person identification. Her research has been reported by Finnish TV programs, newspapers and MIT Technology Review.
Speech Title: "Face Anti-Spoofing with Remote Heart Rate Estimation from Videos"
Abstract: Face biometric systems should be robust to spoofing attacks, including a falsified image, video or 3D mask of a valid user. Some widely used approaches for differentiating genuine faces from fake ones has been to capture their inherent differences in (2D or 3D) texture using local descriptors or depth information. This talk touches the topic in a very different viewangle, from detecting pulse from face videos. Based on the fact that a pulse signal exists in a real living face but not in any mask or print material, the remote heart rate estimation method could be a generalized solution for face liveness detection. This talk starts from remote heart rate measure method which works in realistic situations, to adapting the method to face mask anti-spoofing. Experiments and comparison show interesting and promising results for potential real world applications.
Keynote Speaker III
Assoc. Prof. Julian Fierrez
Universidad Autonoma De Madrid, Spain
Julian Fierrez received the MSc and the PhD degrees in telecommunications engineering from Universidad Politecnica de Madrid, Spain, in 2001 and 2006, respectively. Since 2002 he was affiliated as a PhD candidate with the Universidad Politecnica de Madrid, and since 2004 he is at Universidad Autonoma de Madrid, where he is currently an Associate Professor since 2010. From 2007 to 2009 he was a visiting researcher at Michigan State University in USA under a Marie Curie fellowship. His research interests include general signal and image processing, pattern recognition, and biometrics. Since 2016 he is Associate Editor for Elsevier's Information Fusion, IEEE Trans. on Information Forensics and Security, and IEEE Trans. on Image Processing. Prof. Fierrez has been actively involved in multiple EU projects focused on biometrics (e.g. TABULA RASA and BEAT), has attracted notable impact for his research, and is the recipient of a number of distinctions, including: EBF European Biometric Industry Award 2006, EURASIP Best PhD Award 2012, Medal in the Young Researcher Awards 2015 by the Spanish Royal Academy of Engineering, and the Miguel Catalan Award to the Best Researcher under 40 in the Community of Madrid in the general area of Science and Technology. In 2017 he has been also awarded the 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.
Speech Title: "Blockchain and Biometrics: Opportunities and Challenges"
Abstract: We will first discuss opportunities and challenges in the integration of blockchain and biometrics, with emphasis in biometric template storage and protection, a key problem in biometrics still largely unsolved. Blockchain technologies provide excellent architectures and practical tools for securing and managing the sensitive and private data stored in biometric templates, but at a cost. We will then report preliminary experiments studying the key tradeoffs involved in that integration, namely: latency, processing time, economic cost, and biometric performance. The experiments reported are based on a smart contract implemented on Ethereum for biometric template storage, whose cost-performance is evaluated by varying the complexity of state-of-the-art schemes for face and handwritten signature biometrics, including deep learning approaches and databases captured in the wild. Finally, we will discuss that straightforward schemes for data storage in blockchain (i.e., direct and hash-based) may be prohibitive for biometric template storage using state-of-the-art biometric methods, and we will then outline new architectures for overcoming that challenge.
Invited Speaker I
Prof. Xiaoyi Feng
Northwestern Polytechnical University, China
Feng Xiaoyi is currently a professor and doctoral supervisor at the School of Electronic Information, Northwest Polytechnic University. She is deputy director of the Key Laboratory of Aerospace Electronic Information Perception and Optical Control, Ministry of Education. She is also member of the council of the Chinese Society of Image Graphics, and Vice President of Shaanxi Society of Image Graphics. Her research interests include computer vision, image processing and pattern recognition. Her recent research focuses on human-centered computing, including face expression recognition, false face attack recognition, face-based parent-child relationship estimation and so on.
Speech Title: "Face Spoofing Detection based on Deep Learning"
Abstract: As one of the most natural clues
for identifying individuals, face images have been used as
the preferred biometric trait in many identity recognition
systems. However, face spoofing becomes a clear threat for
these recognition systems. In this talk, I will explore the
ideas on how to use deep learning based models to detect
face spoofing in 2D fake face images, videos and 3D face
masks by our research group. Several specific-designed deep
models will be introduced and compared to existing
handcrafted features in the experimental evaluation.