  Given a gallery of face images of missing children, state-of-the-art face
recognition systems fall short in identifying a child (probe) recovered at a
later age. We propose an age-progression module that can age-progress deep face
features output by any commodity face matcher. For time lapses larger than 10
years (the missing child is found after 10 or more years), the proposed
age-progression module improves the closed-set identification accuracy of
FaceNet from 40% to 49.56% and CosFace from 56.88% to 61.25% on a child
celebrity dataset, namely ITWCC. The proposed method also outperforms
state-of-the-art approaches with a rank-1 identification rate from 94.91% to
95.91% on a public aging dataset, FG-NET, and from 99.50% to 99.58% on CACD-VS.
These results suggest that aging face features enhances the ability to identify
young children who are possible victims of child trafficking or abduction.
