A Linked Aggregate Code for Processing Faces (Revised Version)

TL;DR

This paper introduces the Linked Aggregate Code (LAC), a biologically inspired face representation model that predicts human face similarity judgments and reveals perceptual biases, serving as a foundation for further face perception research.

Contribution

The paper presents the LAC model, which links V1 cell responses to face features, and demonstrates its ability to predict human similarity judgments and uncover perceptual biases.

Findings

LAC predicts human performance in face similarity tasks.

LAC reveals perceptual biases such as race and sex.

Model highlights differences between human and machine face perception.

Abstract

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.