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