Work

A new framework for facial age estimation in humans and AIs

Year: 2026

Type: article

Abstract: Apparent facial age plays an important role in social interactions, serving a meaningful marker of biological aging. Although both humans and AIs achieve reasonable accuracy in estimating age from a person's face, performance remains imprecise, leaving substantial room for errors and biases. Drawing on principles from classical psychophysics, we demonstrate that the existing literature on age estimation suffers from a critical theoretical and methodological shortcoming, which casts doubt on established findings. We show that the conventional measure used to benchmark the accuracy of human and AI performance is confounded by response bias. Consequently, we introduce a novel measure that eliminates this confound. A revised framework based on simulated data, reanalysis of existing data, and new experimental results, reveals fresh insights into how facial age is processed by humans and AIs. Our structure opens up new directions for future research and applications in the study of aging.

Source: Scientific Reports

DOI: 10.1038/s41598-026-49573-1

Authors: Tzvi Ganel, Yarden Mazuz, Daniel Algom, Melvyn A. Goodale

Institutions: Ben-Gurion University of the Negev, Tel Aviv University, Achva Academic College, Western University

Language: English

Topic: Face Recognition and Perception

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Israel Science Foundation