Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age

TL;DR

This paper evaluates child face recognition over an 8-year period, analyzing how age progression affects identification accuracy using a longitudinal dataset.

Contribution

It introduces a longitudinal evaluation methodology for child face recognition and provides insights into age-related performance changes.

Findings

Age progression impacts recognition accuracy

Longitudinal dataset reveals performance trends over years

Baseline results establish a foundation for future research

Abstract

The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA database collected by Clarkson University CITeR research group over an 8 year period, at 6 month intervals.