2021 3rd International Conference on Video, Signal and Image Processing

Invited Speakers

Prof. Linlin Shen
Shenzhen University, China

Prof. Linlin Shen is currently the "Pengcheng Scholar" Distinguished Professor at School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. He is also an Honorary professor at School of Computer Science, University of Nottingham, UK and a Consultant on computer vision for Huawei Technology Co. Ltd. He serves as the director of Computer Vision Institute, AI aided Medical Image Analysis & Diagnosis research center and China-UK joint research lab for visual information processing. He also serves as the Co-Editor-in-Chief of the IET journal of Cognitive Computation and Systems. He received the BSc and MEng degrees from Shanghai JiaoTong University, Shanghai, China, and the Ph.D. degree from the University of Nottingham, Nottingham, U.K. He was a Research Fellow with the University of Nottingham, working on MRI brain image processing. His research interests include deep learning, facial analysis and medical image processing. Prof. Shen is listed as the Most Cited Chinese Researcher by Elsevier. He received the Most Cited Paper Award from the journal of Image and Vision Computing. His cell classification algorithms were the winners of the International Contest on Pattern Recognition Techniques for Indirect Immunofluorescence Images held by ICIP 2013 and ICPR 2016.





Prof. Li Wei
Beijing Institute of Technology, China

Wei Li received the B.E.degree in telecommunications engineering from Xidian University, Xi'an, China, in 2007, the M.S. degree in information science and technology from Sun Yat-Sen University, Guangzhou, China, in 2009, and the Ph.D. degree in electrical and computer engineering from Mississippi State University, Starkville, MS, USA, in 2012. Subsequently, he spent 1 year as a Postdoctoral Researcher at the University of California, Davis, CA, USA. He is currently a professor with the School of Information and Electronics, Beijing Institute of Technology. His research interests include hyperspectral image analysis, pattern recognition, and data compression.

He is currently serving as Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing (TGRS), IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), and IEEE Signal Processing Letters (SPL). He has published more than 150 peer-reviewed articles and 100 conference papers totally cited by 7500 times(Google Scholar). He received the JSTARS Best Reviewer in 2016 and TGRS Best Reviewer award in 2020 from IEEE Geoscience and Remote Sensing Society (GRSS), and the Outstanding Paper award at IEEE International Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Whispers), 2019.





Prof. Yu-Dong Zhang
University of Leicester, UK

Prof. Yu-Dong Zhang received the PhD degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA; and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as Professor with School of Informatics, University of Leicester, UK. His research interests include deep learning and medical image analysis.

He is the Fellow of IET (FIET), and Senior Members of IEEE, IES, and ACM. He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Web of Science Highly Cited Researcher”. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in "Top Scientist" in Guide2Research. He is the author of over 250 peer-reviewed articles, including more than 40 “ESI Highly Cited Papers”, and 3 “ESI Hot Papers”. His citation reached 14769 in Google Scholar, and 8657 in Web of Science. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, GCRF, EPSRC, MRC, and British Council.





Prof. Liangxiu Han
Manchester Metropolitan University, UK

Prof. Liangxiu Han has a PhD in Computer Science from Fudan University, Shanghai, P.R. China (2002). Prof. Han is currently a Professor of Computer Science at the Department of Computing and Mathematics, Manchester Metropolitan University. She is a co-Director of Centre for Advanced Computational Science and Deputy Director of ManMet Crime and Well-Being Big Data Centre. Han’s research areas mainly lie in the development of novel big data analytics/Machine Learning/AI, and development of novel intelligent architectures that facilitates big data analytics (e.g., parallel and distributed computing, Cloud/Service-oriented computing/data intensive computing) as well as applications in different domains (e.g. Precision Agriculture, Health, Smart Cities, Cyber Security, Energy, etc.) using various large scale datasets such as images, sensor data, network traffic, web/texts and geo-spatial data. As a Principal Investigator (PI) or Co-PI, Prof. Han has been conducting research in relation to big data/Machine Learning/AI, cloud computing/parallel and distributed computing (funded by EPSRC, BBSRC, Innovate UK, Horizon 2020, British Council, Royal Society, Industry, Charity, respectively, etc.).

Prof. Han has served as an associate editor/a guest editor for a number of reputable international journals and a chair (or Co-Chair) for organisation of a number of international conferences/workshops in the field. She has been invited to give a number of keynotes and talks on different occasions (including international conferences, national and international institutions/organisations). Prof. Han is a member of EPSRC Peer Review College, an independent expert for Horizon 2020 proposal evaluation/mid-term project review, and British Council Peer Review Panel.





Prof. Julian Fierrez
Universidad Autónoma 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 2004 he has been at Universidad Autonoma de Madrid, where he is Associate Professor since 2010. From 2007 to 2009 he was a visiting researcher at Michigan State University in the USA under a Marie Curie fellowship. His research is on signal and image processing, AI fundamentals and applications, HCI, forensics, and biometrics for security and human behavior analysis. He is actively involved in large EU projects in these topics (e.g., BIOSECURE, TABULA RASA and BEAT in the past; now IDEA-FAST, PRIMA and TRESPASS-ETN). Since 2016 he has been Associate Editor for Elsevier's Information Fusion and IEEE Trans. on Information Forensics and Security, and since 2018 also for IEEE Trans. on Image Processing. He has been General Chair of the IAPR Iberoamerican Congress on Pattern Recognition (CIARP 2018) and the Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2019). Since 2020 he is a member of the ELLIS Society. Prof. Fierrez has received best papers awards at AVBPA, ICB, IJCB, ICPR, ICPRS, and Pattern Recognition Letters. He is also recipient of several world-class research 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 was 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. [http://biometrics.eps.uam.es].

Speech Title: Biases in Machine Learning and Responsible Artificial Intelligence
Abstract:
In the last few years, we are witnessing a growing interest in the Artificial Intelligence research community in studying bias effects when machine learning methods are applied on large amounts of data. These bias effects can stem from the data itself or from the learning process, which nowadays is clearly dominated by deep learning methods that most of the time are quite opaque. When those learning processes are related to AI applications dealing with personal information, or whose application affects people’s lives, then biases can result in unfair AI-based automated decision-making processes, very harmful in terms of undesired discrimination among population groups. This keynote will discuss the current state of the topic with special emphasis in AI applications involving face biometrics. Recent methods and approaches to reduce undesired discrimination towards fair biometrics will be also discussed.





Assoc. Prof. Yongqing Huo
University of Electronic Science and Technology of China, China

Yongqing Huo received the B.S. degree in communication engineering and the M.S. and Ph.D. degrees in signal and information processing from the University of Electronic Science and Technology of China, Chengdu, China, in 2002, 2005, and 2007, respectively. In 2008, she joined the University of Electronic Science and Technology of China. From 2011 to 2012, she was a Postdoctoral Researcher with the University of Burgundy, France. She is currently an Associate Professor with the School of information and Communication Engineering, University of Electronic Science Technology of China. Her current research interests include data hiding and HDR imaging.





Assoc. Prof. Jianxin Lin
Hunan University, China

Jianxin Lin received the B.E. and Ph.D. degrees from University of Science and Technology of China (USTC) in 2015 and 2020. He is currently an associate professor at the School of Computer Science and Electronic Engineering, Hunan University, Changsha, China. His research interests include image/video processing, image/video synthesis and few-shot learning.
He has published more than 20 papers on top-tier computer vision and image processing conferences and journals, including CVPR, ECCV, TPAMI, TIP and so on. He also served as reviewers for many top-tier conferences and journals, such as NeuriPS, ICLR, ICML, IJCV and so on.





Assoc. Prof. Bambang Leo Handoko
Bina Nusantara University, Indonesia

Assoc. Prof. Bambang Leo Handoko, academics and practitioners in the field of business, specialty in Auditing. Experience as auditor in public accounting firm, internal auditor for corporation and auditor for securing vital objects of National Police Headquarters. He is an expert in financial audit, cryptocurrencies, financial technology, and e-business. He has had many international publications in reputable journals and proceeding with high index from many citations and acknowledgement from international researchers. He had won a lot of research grant from institution and government. Currently work as Subject Content Coordinator Auditing in Accounting Department, Faculty of Economic and Communication, Bina Nusantara University of Indonesia. He also technical committee in many reputable journal and conference.