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Keynote Speakers

Prof. Ajay Kumar (IEEE Fellow and IAPR Fellow)
The Hong Kong Polytechnic University, China


Ajay Kumar received the Ph.D. degree from The University of Hong Kong in 2001. He was an Assistant Professor with the Department of Electrical Engineering, IIT Delhi, Delhi, India, from 2005 to 2007. He is currently working as a Professor with the Department of Computing, The Hong Kong Polytechnic University. His current research interests are on biometrics with an emphasis on hand biometrics, vascular biometrics, iris, and multimodal biometrics. He holds seven U.S. patents, and has authored on biometrics and computer vision based industrial inspection. He was on the Editorial Board of the IEEE Transactions on Information Forensics and Security from 2010 to 2013, and served on the program committees of several international conferences and workshops in the field of his research interest. He serves as an area chair for CVPR 2019, was the Program Chair for ICEB 2010 (Hong Kong), Program Co-Chair for the IJCB 2011 (Washington DC), ICB 2013 (Madrid), and CVPR 2013-2021 Biometrics Workshops. He has also served as the General Co-Chair of the IJCB 2014 (Tampa), ISBA 2015 (Hong Kong) and General Chair for WIFS 2018 recently held in Hong Kong. He serves on the Editorial Board of Journal of Pattern Recognition Letters and has served on the IEEE Biometrics Council as the Vice President (Publications) during 2011-2015. He is a Fellow of IAPR and IEEE.


Speech Title: "Completely Contactless Palmprint Detection and Identification in the Wild"


Abstract: Ongoing pandemic due to COVID19 has posed increasing challenges for online the personal identification. Conventional face recognition performance has reported significant degradation due to the large-scale use of face masks. Therefore, there has been increasing attention on the alternative biometric modalities that can be acquired under completely contactless imaging and using popularly used smartphones. This talk will discuss recent advancements in contactless palmprint biometrics technologies to meet such expectations for the real-world deployments. During this talk, I will discuss on the open challenges for completely contactless palmprint identification and state-of-the art matching algorithms in area. Accurate detection and alignment of region of interest, especially under dynamic background and scale changes that are inherently introduced in completely contactless imaging, is critical for the success of matching algorithms. Therefore, some of the recently introduced methods to accurately detect palmprint regions from contactless hand images will also be discussed. Experimental results from over eighteen million match scores, under cross-database contactless palmprint matching scenarios, will be discussed to underline reliability our approach over earlier work in this area.


Prof. Changsheng Xu (IEEE Fellow and IAPR Fellow)
Chinese Academy of Sciences
, China


Changsheng Xu is a professor of Institute of Automation, Chinese Academy of Sciences. His research interests include multimedia content analysis/indexing/retrieval, pattern recognition and computer vision. He has hold 50 granted/pending patents and published over 400 refereed research papers including 100+ IEEE/ACM Trans. papers in these areas. Prof. Xu is Editor-in-Chief of Multimedia Systems. He serves/served Associate Editor of IEEE Trans. on Multimedia and ACM Trans. on Multimedia Computing, Communications and Applications. He received the Best Paper Awards of ACM Multimedia 2016 and 2016 ACM Trans. on Multimedia Computing, Communications and Applications. He served as Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair, special session organizer, session chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.


Speech Title: "Connecting Isolated Social Multimedia Big Data"


Abstract: The explosion of social media has led to various Online Social Networking (OSN) services. Today's typical netizens are using a multitude of OSN services. Exploring the user-contributed cross-OSN heterogeneous data is critical to connect between the separated data islands and facilitate value mining from big social multimedia. From the perspective of data fusion, understanding the association among cross-OSN data is fundamental to advanced social media analysis and applications. From the perspective of user modeling, exploiting the available user data on different OSNs contributes to an integrated online user profile and thus improved customized social media services. This talk will introduce a user-centric research paradigm for cross-OSN mining and applications and some pilot works along two basic tasks: (1) From users: cross-OSN association mining and (2) For users: cross-OSN user modeling.


Dr. Vadim Pisarevsky
Institute of Artificial Intelligence and Robotics for Society, China


Vadim Pisarevsky is software engineering manager at Intel Corp. Vadim has graduated from Nizhniy Novgorod State Univ in 1998 with MSc degree in math. He has been a chief architect and leader of OpenCV development team since the beginning of the project in 2000 at Intel. From 2008 till 2016 he has been a principal engineer at Itseez Inc., who have been doing computer vision projects for various customers all over the world using OpenCV, and who also played a big role in creating OpenVX standard. Since 2016 Vadim is back to Intel, keep working on OpenCV as the team lead. He is the co-author of several publications and patents.


Speech Title: "OpenCV 5.x. Status and the Plans"


Abstract: OpenCV (Open Source Computer Vision Library) is one of the most popular computer vision frameworks in the world, developed since 2000. Now the project is developed by the international team with the core teams in US, Russia and China, with a constantly growing role of the community. OpenCV 5.0, scheduled for 2021, is the major milestone of the project. The presentation gives a brief overview of the project, it's organizational structure, the architecture and infrastructure. The key features of the version 5.0 are described, among which are the new 3D module, greatly improved and extended deep learning functionality, plugins, better support for low-power edge platforms and enhanced Python interface. In the final part some plans for OpenCV 5.x and 6.0 are unveiled, and the new functional programming language for computing, CV and ML is introduced.