Prof. Guoying Zhao
University of Oulu, Finland
Guoying Zhao is currently a Professor with the Center for Machine Vision and Signal Analysis, University of Oulu, Finland, where she has been a senior researcher since 2005 and an Associate Professor since 2014. She received the Ph.D. degree in computer science from the Chinese Academy of Sciences, Beijing, China, in 2005. She got the Academy Postdoctoral position in 2007, and in 2011, she was selected to the highly competitive Academy Research Fellow position. She has authored or co-authored more than 210 papers in journals and conferences, and has served as a reviewer for many journals and conferences. Her papers have currently over 11484 citations in Google Scholar (h-index 47). She was general chair for ICBEA 2019, Co-Chair for Late Breaking Results of ICMI 2019, 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 FG 2018, ICPR 2006, ICCV 2009, and SCIA 2013, and authored/edited three books and eight special issues in journals. Dr. Zhao was a Co-Chair of 15 International Workshops / special sessions in top venues, such as ICCV, CVPR, ECCV, ACCV and FG. Her students and researchers are frequent recipients of very prestigious and highly competitive fellowships, such as Academy of Finland Postdoc position, the Nokia Scholarship, Endeavour Research Fellowship, Tauno Tönning Research funding, Kauta Foundation grant and Jorma Ollila grant. She is IEEE Senior Member. Her current research interests include image and video descriptors, gait analysis, dynamic-texture recognition, facial-expression recognition, human motion analysis, and person identification. Her research has been reported by Finnish TV programs, newspapers and MIT Technology Review.
Speech Title: "Facial Micro-Expression Analysis: The Roadmap"
Abstract: Emotions are a central part of human communication, play an important role in everyday social life, and should have a key role in human-computer interactions. Emotions are complicated. Sometimes, people intentionally express their emotions to help deliver the messages and sometimes people would suppress and hide their emotions for different reasons. This talk introduces the work towards reading hidden emotions from computer vision viewpoint, including the roadmap of spontaneous micro-expression analysis, from dataset collection, spotting and recognition, to cross-datasets recognition and action units detection, and discusses the open problems in this area.
Prof. Banghua Yang
Shanghai University, China
Prof Banghua is a Professor in the Department of Automation, Shanghai University, Shanghai, China. Dr Yang has been working on motor imagery brain computer interfaces since 2003. Her research interests include brain-computer interfaces, EEG signal processing, biomedical signal processing, virtual reality and rehabilitation application. She has undertaken three National Natural Science Funds as the project leader and five other national projects. She won the talent program of Shanghai Pujiang. Prof Yang has published over 100 articles in peer-reviewed core national and international journals. She is a reviewer of many national and international journals. She edited 3 textbooks. Prof Yang and her students have developed many processing algorithms for EEG signal and rehabilitation application by combining BCIs with virtual reality. Her research group attended the 2th and 3th China BCI Competition, which was held by Tsinghua University in Beijing and was supported by National Science Foundation of China. Her research team obtained the third prize in the robot control based on BCI. She has been invited to make more than 30 academic reports at domestic and international conferences.
Speech Title: "Application of BCI Technique Combined with VR in Intelligent Diagnosis and Rehabilitation"
Abstract: Brain-computer interface (BCI) is an important aspect of brain science research which is an international frontier field. It is a human-computer interaction mode developed in recent years. Through this technology, people can directly express or manipulate other devices by the brain without the need for the language or body movement. BCI technology can be used as a new mode for active recovery of the brain disease, by decoding the brain signals and combining the closed-loop feedbacks such as VR through vision, touch or hearing. It can make patients repeat training on specific brain regions. Based on brain plasticity, this brain training can stimulate the reconstruction of the damaged neural function. This report will introduce the basic principle of BCI, the core artificial intelligence decoding method, the specific application of BCI combined with VR on the typical medical and industrial fields such as rehabilitation of stroke patients, rehabilitation of drug addiction diagnosis, and diagnosis of mental depression patients.