Speaker - S. Zafeirou (Senior Lecturer (Associate Professor ), Imperial College London, UK)

ibug.doc.ic.ac.uk/people/szafeiriou

Title - Building the first large scale 3D morphable model of faces.

Abstract - 3D Morphable Models (3DMMs) are powerful 3D statistical models of human face shape and texture. 3DMMs have been widely applied in numerous areas, such as computer vision, human behavioral analysis, and computer graphics etc. In particular, a 3DMM used in an analysis-by-synthesis framework is still among the state-of-the-art methods for performing reconstruction of the 3D facial surface from single images captured under unconstrained conditions. Until now, the most widely known 3DMM was the so-called Basel Model, which is built from 200 people. In this presentation, I will talk about the first large scale 3DMM that was built by my group from scans of around 10,000 people. I will talk how we automatically built it, the lessons we learnt and the applications that such a large-scale 3DMM could have in computer vision, as well as in automatizing surgery planning.

Short CV - Stefanos Zafeiriou (s.zafeiriou@imperial.ac.uk) (M'09) is currently an Associate Professor in Pattern Recognition/Statistical Machine Learning for Computer Vision with the Department of Computing, Imperial College London, London, U.K, and a Distinguishing Research Fellow with University of Oulu under Finish Distinguishing Professor Programme. He was a recipient of the Prestigious Junior Research Fellowships from Imperial College London in 2011 to start his own independent research group. He has received various awards during his doctoral and post-doctoral studies. He currently serves as an Associate Editor of the IEEE Transactions on Cybernetics the Image and Vision Computing Journal. He has been a Guest Editor of over five journal special issues and co-organized over eight workshops/special sessions on face analysis topics in top venues, such as CVPR/FG/ICCV/ECCV (including two very successfully challenges run in ICCV'13 and ICCV'15 on facial landmark localisation/tracking). He has co-authored over 45 journal papers mainly on novel statistical machine learning methodologies applied to computer vision problems, such as 2-D/3-D face analysis, deformable object fitting and tracking, shape from shading, and human behaviour analysis, published in the most prestigious journals in his field of research, such as the IEEE T-PAMI, the International Journal of Computer Vision, the IEEE T-IP, the IEEE T-NNLS, the IEEE T-VCG, and the IEEE T-IFS, and many papers in top conferences, such as CVPR, ICCV, ECCV, ICML. His students are frequent recipients of very prestigious and highly competitive fellowships, such as the Google Fellowship, the Intel Fellowship, and the Qualcomm Fellowship. He is the General Chair of BMVC 2017.