Human Identification via Gait Recognition

Slides (pdf)Video


Human identification at a distance is a very challenging task, which has long been a popular research topic in the field of computer vision. The gait sequences of different people can be very distinctive, which makes gait an important body characteristic that can be used for human identification. In this lecture, I will first introduce the brief history of gait-based human identification and list out the challenges that lie in this field, such as cross-view and cross walking condition gait recognition. Then I will share a comprehensive survey on the different modules of a gait-based human identification system. Specifically, I will summarize both the traditional approaches and the advanced deep learning based approaches for gait-based human identification. In particular, such novel deep learning models can achieve an average accuracy of 98% under identical view conditions and 91% for cross-view scenarios in the database with more than 4000 people, which are much better than the previously reported results. Afterwards, we discuss the applications of gait recognition at a distance in different kinds of visual tasks. Finally, I will share some suggestions of employing gait recognition in practice and indicate potential directions of this area for future work.


Dr. Liang Wang received both the B. Eng. and M. Eng. degrees from Anhui University in 1997 and 2000 respectively, and the PhD degree from the Institute of Automation, Chinese Academy of Sciences (CAS) in 2004. From 2004 to 2010, he worked as a Research Assistant at Imperial College London, United Kingdom and Monash University, Australia, a Research Fellow at the University of Melbourne, Australia, and a lecturer at the University of Bath, United Kingdom, respectively. Currently, he is a full Professor of Hundred Talents Program at the National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P. R. China. His major research interests include machine learning, pattern recognition and computer vision. He has widely published at highly-ranked international journals such as IEEE TPAMI and IEEE TIP, and leading international conferences such as CVPR, ICCV and ICDM. He has obtained several honors and awards such as the Special Prize of the Presidential Scholarship of Chinese Academy of Sciences. He is currently a Senior Member of IEEE and a Fellow of IAPR, as well as a member of BMVA. He is an associate editor of IEEE Transactions on Cybernetics and IEEE Transactions on Information Forensics and Security.


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Liang Wang

Liang Wang
Chinese Academy of Sciences, China