Recognising person’s identity is an important goal in Biometrics (e.g., Face, Palm print, Body images). In order to quantify the extracted Biometrics features, machine learning technologies have been widely employed and improved. This talk will introduce the major machine technologies for person identification, including learning-based feature extraction, distance metric learning, ensemble based methods, and online/incremental learning. In particular, the talk will give a particular focus on the machine learning for the person re-identification problem, which matches people across non-overlapping camera views at different locations and different time in a large distributed space. Person re-identification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, gesture, lighting, background clutter, and occlusion. To overcome these challenges, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions is critically important. We will introduce recent research works using machine learning for robust cross-view person image matching in details.
Wei-Shi Zheng received the PhD degree in applied mathematics from Sun Yat-Sen University in 2008. He is now a Professor at Sun Yat-sen University. He had been a Postdoctoral Researcher on the EU FP7 SAMURAI Project at Queen Mary University of London and an Associate Professor at Sun Yat-sen University after that. He has now published more than 90 papers, including more than 50 publications in major journals (TPAMI,TNN,TIP,PR) and top conferences (ICCV, CVPR,IJCAI,AAAI). He has joined the organisation of four tutorial presentations in ACCV 2012, ICPR 2012, ICCV 2013 and CVPR 2015 along with other colleagues. His research interests include person re-identification and activity understanding in visual surveillance. He has joined Microsoft Research Asia Young Faculty Visiting Programme. He received the outstanding reviewer award in ECCV 2016. He is a recipient of Excellent Young Scientists Fund of the National Natural Science Foundation of China, and a recipient of Royal Society-Newton Advanced Fellowship. Homepage: http://isee.sysu.edu.cn/~zhwshi/