Three tutorials will be held as a part of FG 2015. Information on the tutorials is provided below.
Date and time: Monday, 4 May 2015, 9:00-12:30
Presenters: Kevin Bowyer
Tutorial Description: This tutorial is intended for attendees who may be familiar with some other area of biometrics, and who want to understand the basic principles of iris recognition and to be aware of current major research directions. The tutorial includes a historical perspective on the origin and development of iris recognition, an explanation of the principles of the Daugman approach to iris recognition, and an introduction to major current research directions.
About the presenter: Kevin Bowyer is the Schubmehl-Prein Professor and Chair of the Department of Computer Science and Engineering at the University of Notre Dame. Professor Bowyer’s research interests touch on various aspects of computer vision and pattern recognition, including biometrics and data mining. He is a Fellow of the IAPR, a Fellow of the IEEE , a Golden Core member of the IEEE Computer Society and received a 2014 IEEE Computer Society Technical Achievement Award “for pioneering contributions to the science and engineering of biometrics”. Professor Bowyer is serving as General Chair of the 2015 IEEE International Conference on Automated Face and Gesture Recognition. He was previously General Chair of the 2011 International Joint Conference on Biometrics, Program Chair of the 2011 Automated Face and Gesture Recognition conference, a founding General Chair of the IEEE Biometrics Theory Applications and Systems conference series, and a past EIC of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the IEEE Biometrics Compendium. Professor Bowyer is a member of the Editorial Board for IEEE Access, IEEE’s first “rapid publication, open access, mega-journal”. Professor Bowyer is co-editor of the Handbook of Iris Recognition.
Discriminative Learning for Single‐Sample Face Recognition
Date and time: Friday, 8 May 2015, 9:00-12:30
Presenters: Jiwen Lu and Weihong Deng
Tutorial Description: Single‐sample face recognition (SSFR) is an important and challenging problem in practical face recognition. Most existing face recognition methods usually require that there are multiple samples per person available for discriminative feature extraction in the training stage. In many real face recognition applications such as law enhancement, e‐passport, and ID card identification, however, this requirement does not hold as there is only one single sample per person enrolled or recorded in these systems. Hence, how to learn discriminative feature representations remains an important and challenging problem for SSFR.
In this tutorial, we will first review the state‐of‐the‐art SSFR techniques. Then, we will introduce some of our newly proposed SSFR methods two aspects: discriminative feature learning and discriminative classifier learning, which differ in learning discriminative descriptors and models for SSFR. Moreover, recent methods, such as deep metric learning and generic 3D model, are also tutored to address the occlusion and pose variations for unconstrained SSFR. Lastly, we will discuss some open problems to understand how to develop more advanced discriminative feature and classifier learning algorithms for single‐sample face recognition systems in the future. More details of this tutorial can be found here.
About the presenters: Jiwen Lu is currently a Research Scientist at the Advanced Digital Sciences Center (ADSC), Singapore. His research interests include computer vision, pattern recognition, and machine learning. He has authored or co‐authored over 100 scientific papers in these areas, where more than 30 papers were published in the IEEE Transactions journals (PAMI/TIP/TCSVT/TIFS) and the top‐tier computer vision conferences (ICCV/CVPR/ECCV). He served as an Area Chair for the 2015 IEEE International Conference on Multimedia and Expo (ICME 2015) and the 2015 IAPR/IEEE International Conference on Biometrics (ICB 2015), and a Special Session Chair for 2015 IEEE Conference on Visual Communications and Image Processing (VCIP 2015). He organizes several workshops/competitions at some international conferences such as ICME2014, ACCV2014, IJCB2014 and FG2015. He was a recipient of the First‐Prize National Scholarship and the National Outstanding Student Award from the Ministry of Education of China in 2002 and 2003, the 2012 Best Student Paper Award from PREMIA of Singapore, the Top 10% Best Paper Award from MMSP 2014, respectively. Recently, he gives tutorials at CVPR 2015, ACCV 2014, ICME 2014 and IJCB 2014.
Weihong Deng received the B.E. degree in information engineering and the Ph.D. degree in signal and information processing from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2004 and 2009, respectively. From Oct. 2007 to Dec. 2008, he was a postgraduate exchange student in the School of Information Technologies, University of Sydney, Australia, under the support of the China Scholarship Council. He is currently an associate professor in School of Information and Telecommunications Engineering, BUPT. His research interests include statistical pattern recognition and computer vision, with a particular emphasis in face recognition. He has published over 30 technical papers in international journals and conferences, including a technical comment on face recognition in SCIENCE magazine. He also serves as the reviewer for several international journals, such as IEEE TPAMI, IJCV, IEEE TIP, IEEE TIFS, PR, and IEEE TSMC‐B. His dissertation titled “Highly accurate face recognition algorithms” was awarded the Outstanding Doctoral Dissertation by Beijing Municipal Commission of Education in 2011. He has been supported by the program for New Century Excellent Talents by the Ministry of Education of China since 2013.
BEAT: An online web-platform for reproducible research
Date and time: Friday, 8 May 2015, 14:00-18:30
Presenters: Andre Anjos and Sebastien Marcel
Tutorial Description: This tutorial will present the BEAT platform for online reproducible research, introducing concepts and providing an initial hands-on experience. The BEAT platform allows novice and advanced researchers to: (1) benchmark systems and components; (2) run comparative evaluations; (3) attest (certify) toolchains; (4) provide educational material for new-comers in pattern recognition and (5) optimize algorithms and systems. All these tasks can be accomplished without installing additional software on the users computer, running exclusively from the web browser. The BEAT platform naturally enforces important research aspects such as reproducibility and component re-use. More details about the tutorial can be found here. Slides from the tutorial are available here.
About the presenters:
Andre Anjos received his Ph.D. degree in signal processing from the Federal University of Rio de Janeiro in 2006. He joined the ATLAS Experiment at European Centre for Particle Physics (CERN, Switzerland) from 2001 until 2010 where he worked in the development and deployment of the Trigger and Data Acquisition systems that are nowadays powering the discovery of the Higgs boson. During his time at CERN, Andre studied the application of neural networks and statistical methods for particle recognition at the trigger level and developed several software components still in use today. In 2010, Andre joined the Biometrics Group at the Idiap Research Institute where he works mostly with face biometrics. His current interests include reproducible research in biometrics, anti-spoofing and recognition using faces, pattern recognition, image processing and machine learning. Andre currently leads the design and implementation of the BEAT platform for evaluation and testing. He also serves as reviewer for several scientific journals in pattern recognition, image processing and biometrics.
Sebastien Marcel received the Ph.D. degree in signal processing from Universit_e de Rennes I in France (2000) at CNET, the research center of France Telecom (now Orange Labs). He is currently interested in pattern recognition and machine learning with a focus on biometrics. He is a senior researcher at the Idiap Research Institute (Switzerland), where he heads a research team and conducts research on face recognition, speaker recognition, vein recognition and spoofing attacks detection. In 2010, he was appointed Visiting Associate Professor at the University of Cagliari (IT) where he taught a series of lectures in face recognition. He is also lecturer at the Ecole Polytechnique Federale de Lausanne (EPFL) where he is teaching on “Fundamentals in Statistical Pattern Recognition”. He serves on the Program Committee of several scientific journals and international conferences in pattern recognition and computer vision. He is Associate Editor of IEEE Transaction on Information Forensics and Security (TIFS) since 2013. He is also co-Editor of the upcoming “Handbook of Biometric Anti-Spoofing” with Prof M. Nixon and Prof. S.Z. Li that will be published by Springer. Finally, he is Guest Editor of an IEEE TIFS Special Issue on “Biometric Spoofinng and Countermeasures”. Sebastien Marcel is the principal investigator of international research projects including MOBIO (EU FP7 Mobile Biometry – http://www.mobioproject.org), TABULA RASA (EU FP7 Trusted Biometrics under Spoofing Attacks – http://www.tabularasa-euproject.org) and BEAT (EU FP7 Biometrics Evaluation and Testing). Finally he is also the Director of the Swiss Center for Biometrics Research and Testing.