Event Location: NSH 1507
Bio: Simon Prince was an undergraduate at UCL where he studied Psychology. His doctoral work was at the University of Oxford, in the Department of Experimental Psychology where he investigated human stereo vision using psychophysics. He subsequently worked in the Laboratory of Physiology in Oxford for two years as a post-doc with Andrew Parker studying stereo vision using single unit electro-physiology. In 2001 he became a post-doctoral research fellow in the Department of Electrical and Computer Engineering in the National University of Singapore working on augmented reality. Following this, he moved to Toronto, Canada, where he worked as a post-doc in computer vision for James Elder in the Centre for Vision Research in York University. Since 2005 he has been a faculty member in the department of computer science at University College London. His current interests include image segmentation, face recognition, optical tomography and object recognition.

Abstract: Faces are one of the most studied object classes in computer vision. Performance is very good for tasks such as identity recognition and gender classification when the pose, lighting and expression are controlled. However, in uncontrolled conditions, these tasks remain challenging. Part of the reason for this limitation is the choice of representation: for example, faces have variously been modeled as subspaces and constellations of features, but these representations have only a limited ability to describe uncontrolled facial images. In this talk, I will present several experiments in which we have investigated representing faces with a
regular grid of patches. This type of model can better capture the complex multimodal appearance of uncontrolled faces. I will present models for both gender recognition (or more generally classification of facial characteristic) and pose estimation (regression). I will also show how to extend these patch-based models to allow generation of near photo-realistic images of novel faces.