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Given a gallery of face images of missing children, state-of-the-art facerecognition systems fall short in identifying a child (probe) recovered at alater age. We propose a feature aging module that can age-progress deep facefeatures output by a face matcher. In addition, the feature aging module guidesage-progression in the image space such that synthesized aged faces can beutilized to enhance longitudinal face recognition performance of any facematcher without requiring any explicit training. For time lapses larger than 10years (the missing child is found after 10 or more years), the proposedage-progression module improves the closed-set identification accuracy ofFaceNet from 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a childcelebrity dataset, namely ITWCC. The proposed method also outperformsstate-of-the-art approaches with a rank-1 identification rate of 95.91%,compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to99.50%, on CACD-VS. These results suggest that aging face features enhances theability to identify young children who are possible victims of childtrafficking or abduction.

Child Face Age-Progression via Deep Feature Aging