Learning on Riemannian Manifold for Video-based Face Recognition

Date: 
6.8.2015 13:15
Place: 
TS127

 

Infotech Oulu Lecture Series

Date: Thursday, August 6, 2015
Time: 13:15 - 14:00
Room: TS127

Lecturer: Associate Professor Ruiping Wang, Chinese Academy of Sciences

 

Abstract

Recently the ubiquitous use of video capturing devices is shifting the focus of face recognition research from image-based scenarios to video-based ones. In this talk, I will introduce recent progresses in our group towards this topic. By simply treating video as image set, our works mainly deal with the problem of robust image set modeling and efficient metric learning for classification. Specifically, for robust set modeling, we propose to use the natural second-order statistic - covariance matrix (a.k.a SPD matrix) and further the Gaussian distribution model as set features to characterize the data structure of the image set. Then under the framework of Riemannian geometry and information geometry, the covariance matrices or Gaussian models are embedded into some certain Riemannian manifolds, where Riemannian kernels can be derived based on valid Riemannian metrics and thus facilitate learning algorithms originally developed in Euclidean space. For set classification, the problem is naturally formulated as discriminative metric learning on such Riemannian manifolds. Here, the notion “metric learning” is associated with different application scenarios. We have considered both cases of video vs. video matching and still image vs. video (e.g. image as gallery and video as probe). For large scale face video retrieval applications, we further develop corresponding hashing methods to learn discriminative compact binary codes for highly efficient video search.

Bio

Ruiping Wang is an Associate Professor at the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). Prior to joining ICT in July 2012, he was a postdoctoral researcher with the Tsinghua University from July 2010 to June 2012. He also spent one year working as a Research Associate with the University of Maryland, College Park, from Nov. 2010 to Oct. 2011. He received the B.S. degree in Applied Mathematics from Beijing Jiaotong University in 2003, and the Ph.D. degree in Computer Science from ICT, CAS, in 2010. He has published more than 30 papers in peer-reviewed journals and conferences, including IEEE TPAMI, TIP, TMM, PR, CVPR, ICCV, and has received the Best Student Poster Award Runner-up from IEEE CVPR 2008 for the work on Manifold-Manifold Distance. He serves as regular reviewer/PC member for a number of leading journals and conferences, e.g. IEEE TPAMI, TIP, TCSVT, TMM, TNNLS, IJCV, ICCV, CVPR, ECCV. He has organized tutorials in ACCV 2014 and CVPR 2015 with his colleagues. He has given invited talks in workshops of ICME 2014 and ACCV 2014. His current research interests include video-based face recognition/retrieval, facial expression analysis, image set classification, distance metric learning, and manifold learning.

More information: Guoying Zhao

 

Last updated: 30.7.2015