Child Face Age-Progression via Deep Feature Aging
This addresses the challenge of longitudinal face recognition for missing children, aiding in child trafficking or abduction cases, with incremental improvements over existing methods.
The paper tackles the problem of identifying missing children after long time lapses by proposing a feature aging module that age-progresses deep face features, improving closed-set identification accuracy for FaceNet from 16.53% to 21.44% and for CosFace from 60.72% to 66.12% on a child celebrity dataset.
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 a feature aging module that can age-progress deep face features output by a face matcher. In addition, the feature aging module guides age-progression in the image space such that synthesized aged faces can be utilized to enhance longitudinal face recognition performance of any face matcher without requiring any explicit training. 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 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-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 to 99.50%, 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.