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Abstract

Individual faces vary considerably in both the duality and quantity of the information they contain for recognition and for viewpoint generalization.
In the present study. we assessed the typicality, recognizability, and viewpoint generalizability of individual faces using data from both human observers and
from a computational model of face recognition across viewpoint change. The two-stage computational model incorporated a viewpoint alignment operation
and a recognition-by- interpolation operation. An interesting aspect of this particular model is that the effects of typicality it predicts at the alignment and
recognition stages dissociate, such that face typicality is beneficial for the success of the alignment process, but is adverse for the success of the recognition
process. We applied a factor analysis to the covariance data for the human- and model-derived face measures across the different viewpoints and found
two axes that appeared consistently across all viewpoints. Projection scores for individual faces on these axes (i.e. the extent to which a face's 'performance
profile' matched the pattern of human- and model-derived scores on that axis), correlated across viewpoint changes to a much higher degree than did the
raw recognizability scores of the faces. These results suggest that the stimulus information captured in the model measures may underlie distinct and
dissociable aspects of the recognizability of individual faces across viewpoint change.