Under controlled conditions, today's face recognition and verification algorithms can provide reasonable performance. However, in an unconstrained environment, there are various artifacts that can make recognising (or verifying) faces a difficult task. As a result faces may be captured under a variety of poses, illumination conditions, resolutions and degrees of blur, all of which provide challenges to current recognition algorithms.
Pose variation in particular is a hard problem to deal with. A widely-used approach is to attempt to warp the probe image so that it has the same pose as the gallery image. We have done this both in 2D, using an Active Appearance Model or one of its many variants, and in 3D, by fitting a 3D face model to the probe image, and then rotating it to a frontal pose to match the gallery image. Some of our earlier AAM software is available in the RAVL library
A related project concerns the use of 3D face shape and/or appearance in connection with trying to understand the origins of the indigenous population of the British Isles.
2D AAM-based facial recognition
3D facial mapping