Accurate estimation of an individual’s age is essential for studies in ecology, behavior, and conservation. However, when birth dates are unknown, estimating chronological age often relies on postmortem morphological analyses or invasive and burdensome techniques.
In this study, we explore the potential of deep learning applied to photographic portraits for non-invasive prediction of chronological age.