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 of face images obtained under tightly controlled viewing conditions. The model effectively constructs a view space for novel faces by interpolating view spaces of familiar ones. 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° 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