Eigenfaces for Familiarity

A previous experiment tested subjects’ new/old judgments of previously-studied faces, distractors, and morphs between pairs of studied parents. We examine the extent to which models based on principal component analysis (eigenfaces) can predict human recognition of studied faces and false alarms to the distractors and morphs. We also compare eigenface models to the predictions of previous models based on the positions of faces in a multidimensional “face space” derived from a multidimensional scaling (MDS) of human similarity ratings. We find that the error in reconstructing a test face from its position in an “eigenface space” provides a good overall prediction of human familiarity ratings. However, the model has difficulty accounting for the fact that humans false alarm to morphs with similar parents more frequently than they false alarm to morphs with dissimilar parents. We ascribe this to the limitations of the simple reconstruction error-based model. We then outline preliminary work to improve the fine-grained fit within the eigenface-based modeling framework, and discuss the results’ implications for exemplarand face space-based models of face processing.