Face Tracking and Pose Representation . British Machine Vision Conference, Edinburgh, Scotland, 1996. Valentin and Abdi also show that the number of previously seen views of the face and which particular viewpoints were previously seen significantly affects performance. Finally McKenna et al. Here previously unseen views tend to cluster around the stored views of one particular individual and probabilistic analysis leads to recognition.
Identification is then a matter of matching an unseen view to a point on the characteristic.
Face Tracking and Pose Representation . British Machine Vision Conference, Edinburgh, Scotland, September 1996. Finally , in section 5 we describe how such representations may be used for classifying faces through various attributes. We do not attempt to construct an optimal eigenspace for recognition, rather we use a small but representative sample of our facial image set to form an eigenspace. Typically we sample less than one hundred images from an image set of over 500.