Face Typicality–Distinctiveness Norms for the 304 Front-View Faces of the Glasgow Unfamiliar Face Database

Face typicality and distinctiveness are key facial attributes that influence face recognition performance and the formation of social impressions. The present study aimed to provide normative data for these dimensions, offering a useful resource for face recognition research. Using a 7-point Likert scale, adult participants rated 304 front-facing faces from the Glasgow Unfamiliar Face Database (GUFD) for typicality–distinctiveness. Results indicated that the subjective rating method produced reliable estimates, with meaningful variability observed along the typicality–distinctiveness continuum. Highly distinctive faces were more sparsely represented in the database. These norms can support principled stimulus selection and improved methodological control in empirical research with faces.

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Published
2026-01-26
DOI
https://doi.org/10.3390/data11020024
Primary Topic
Face Recognition and Perception
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article
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article

Face Typicality–Distinctiveness Norms for the 304 Front-View Faces of the Glasgow Unfamiliar Face Database

Susana Araújo, Francisco Cruz, Paulo Ventura
Data
Face Recognition and Perception
article

Face Typicality–Distinctiveness Norms for the 304 Front-View Faces of the Glasgow Unfamiliar Face Database

Susana Araújo, Francisco Cruz, Paulo Ventura
article en

Abstract

Face typicality and distinctiveness are key facial attributes that influence face recognition performance and the formation of social impressions. The present study aimed to provide normative data for these dimensions, offering a useful resource for face recognition research. Using a 7-point Likert scale, adult participants rated 304 front-facing faces from the Glasgow Unfamiliar Face Database (GUFD) for typicality–distinctiveness. Results indicated that the subjective rating method produced reliable estimates, with meaningful variability observed along the typicality–distinctiveness continuum. Highly distinctive faces were more sparsely represented in the database. These norms can support principled stimulus selection and improved methodological control in empirical research with faces.

DataVol. 11(2)
University of Lisbon (PT)
Reduced inequalities
Openalex Percentile: Top 78%
Face Recognition and Perception
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