Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models.

ABSTRACT: Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look and whether they appear to show the same person. Principal component-based three-dimensional (3D) morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a "face space" based on these dimensions capture the similarity relationships humans perceive among faces? To answer this, we designed a behavioral task to collect dissimilarity and same/different identity judgments for 232 pairs of realistic faces. Stimuli sampled geometric relationships in a face space derived from principal components

SUBMITTER: Jozwik KM 

PROVIDER: S-EPMC9271164 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature