A Linked Aggregate Code for Processing Faces (Revised Version)
This work addresses face perception modeling for cognitive science and computer vision, offering a biologically plausible starting point for further studies, though it is incremental in its approach.
The authors tackled the problem of modeling face representation by comparing a biologically-inspired Linked Aggregate Code (LAC) model to human similarity judgments, finding that it predicted human performance for similar faces and revealed dimensions like sex and race without training, but did not capture human racial bias.
A model of face representation, inspired by the biology of the visual system, is compared to experimental data on the perception of facial similarity. The face representation model uses aggregate primary visual cortex (V1) cell responses topographically linked to a grid covering the face, allowing comparison of shape and texture at corresponding points in two facial images. When a set of relatively similar faces was used as stimuli, this Linked Aggregate Code (LAC) predicted human performance in similarity judgment experiments. When faces of perceivable categories were used, dimensions such as apparent sex and race emerged from the LAC model without training. The dimensional structure of the LAC similarity measure for the mixed category task displayed some psychologically plausible features but also highlighted differences between the model and the human similarity judgements. The human judgements exhibited a racial perceptual bias that was not shared by the LAC model. The results suggest that the LAC based similarity measure may offer a fertile starting point for further modelling studies of face representation in higher visual areas, including studies of the development of biases in face perception.