A similarity-based method for the generalization of face recognition over pose and expression

Human observers are capable of recognizing a face seen only once before when confronted with it subsequently under different viewing conditions. We constructed a working computational model of such generalization from a single view, and tested it on a homogeneous database efface images obtained under tightly controlled viewing conditions. The model effectively constructs a view space for novel faces by interpolating view spaces of familiar ones [5]. Its performance - 30% error rate in one out of 18 recognition, and 8% in one out Of three discrimination - is encouraging, given that it reflects generalization from a single view/expression to a range of +/-34 degrees rotation in depth and to two additional expressions. For comparison, human subjects in the one out of three task involving only viewpoint changes exhibit a 3% error rate [11].