From Lukas-Kanade to Mnemonic Descent and Deep Dense Shape Regression: A brief history of (deformable) image alignment.

Stefanos Zafeiriou
(Imperial College London, UK)


Construction and fitting of Statistical Deformable Models (SDM) is in the core of computer vision and image analysis discipline. It can be used to estimate the object's shape, pose, parts and landmarks using only static imagery captured from monocular cameras. One of the first and most popular families of SDMs is that of Active Appearance Models. AAM uses a generative parameterization of object appearance and shape. The fitting process of AAMs is usually conducted by solving a non-linear optimization problem. In this talk I will start with a brief introduction to Lukas-Kanade and AAMs and I will continue with describing supervised methods for AAM fitting. Subsequently, under this framework, I will motivate current techniques developed in my group that capitalize on the combined power of Deep Convolutional Neural Networks (DCNN) and Recurrent NN (RNNs) for optimal deformable object modeling and fitting.


Stefanos P. Zafeiriou (M'09) is currently a Senior Lecturer (USA equivalent to 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 was the recipient of the President's Medal for Excellence in Research Supervision for 2016. 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 six journal special issues and co-organised over nine 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 50 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 has more than 3300 citations to his work, h-index 30. He is the General Chair of BMVC 2017.