Learning to Recognize Faces Despite Within-Person Variability in Appearance: A Developmental Approach

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Recognizing faces is challenging: Two pictures of the same person can look very different and pictures of two people can look very similar. Nonetheless, adults can recognize thousands of faces despite within-person variability in appearance-an ability we rely on to find a friend in a busy train station, follow movie plots, and greet neighbours. This skill for recognizing familiar faces creates the illusion of expertise for all faces, justifying the use of photo ID in settings that involve unfamiliar faces (e.g., when accessing health care or buying age-restricted goods; border security). Adults make many errors when asked whether photos of unfamiliar faces belong to the same person or two different people; a simple change in lighting or expression can be misperceived as a change in identity. A spate of research is examining adults' mental representation of unfamiliar vs. familiar faces and the processes by which a newly encountered face becomes familiar. Significant advances in machine learning have informed theoretical models of face recognition. Developmental research is essential to enrich our understanding of adults' expertise, improve machine learning, and identify conditions under which children make errors. I discovered that matching identity for unfamiliar faces has a prolonged development during childhood, as does the ability to learn a newly encountered face. By 6 years of age, but not younger, children recognize highly familiar faces (their parent/teacher) with ease. Informed by state-of-the-art machine learning algorithms, my proposal comprises three projects to examine the prolonged development of face learning and recognition during childhood. Project A uses head-mounted cameras to discover age-related changes in children's attention to faces (vs. objects) when interacting with adults and peers. I will examine whether experience impacts children's ability to match identity in unfamiliar faces and recognize highly familiar faces. Project B focuses on age-related changes in sensitivity to critical features (e.g., lip thickness, eye colour)-features that govern adults' perception of identity in both unfamiliar and familiar faces. I will examine whether children are less sensitive to critical features than adults and whether critical features differ for child vs. adult faces. Project C will examine the influence of contextual cues on children's face learning and recognition. I will discover whether children benefit more than adults from biographical information, learning faces in groups, and seeing familiar faces in the context in which they were learned. The results will provide novel insights about how face recognition becomes finely tuned during childhood, informing theoretical models and leading to better artificial intelligence.