Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements

TL;DR

This study introduces a unified computational approach combining face image variance and eye-tracking attention maps, suggesting face recognition is better modeled as pattern-based rather than feature-based.

Contribution

It presents a novel multidimensional face-space model integrating eye movement data to analyze face recognition strategies.

Findings

Pattern-based model better explains human face recognition

Eye movements focus on eyes, nose, mouth regions

Emulation of human recognition system achieved

Abstract

Research on human face processing using eye movements has provided evidence that we recognize face images successfully focusing our visual attention on a few inner facial regions, mainly on the eyes, nose and mouth. To understand how we accomplish this process of coding high-dimensional faces so efficiently, this paper proposes and implements a multivariate extraction method that combines face images variance with human spatial attention maps modeled as feature- and pattern-based information sources. It is based on a unified multidimensional representation of the well-known face-space concept. The spatial attention maps are summary statistics of the eye-tracking fixations of a number of participants and trials to frontal and well-framed face images during separate gender and facial expression recognition tasks. Our experimental results carried out on publicly available face databases have indicated that we might emulate the human extraction system as a pattern-based computational method rather than a feature-based one to properly explain the proficiency of the human system in recognizing visual face information.