Face Recognition from Unconstrained Images: Progress with Prototypes

Abstract

Artificial face recognition systems typically do not attempt to handle very variable images. By comparison, human perceivers can recognize familiar faces over much more varied conditions. We describe a prototype face representation based on simple image-averaging. We have argued that this forms a good candidate for understanding human face perception. Here we examine the stability of these representations by asking (i) how quickly they converge; and (U) how resistant they are to contamination due to previous misidentifications. We conclude that face averages provide promising representations for use in artificial recognition