Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age
This addresses the need for reliable child identification in emerging applications, but appears incremental as it applies a longitudinal approach to an existing dataset.
The study tackled the problem of child face recognition by evaluating enrollment and verification accuracy using a longitudinal dataset collected over 8 years at 6-month intervals, focusing on the impact of underlying age.
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.