Identifying Missing Children: Face Age-Progression via Deep Feature Aging

摘要

Given a face image of a recovered child at age ageprobe, we search a gallery of missing children with known identities and age agegallery at which they were either lost or stolen in an attempt to unite the recovered child with his family. We propose a feature aging module that can age-progress deep face features output by a face matcher to improve the recognition accuracy of age-separated child face images. In addition, the feature aging module guides age-progression in the image space such that synthesized aged gallery faces can be utilized to further enhance cross-age face matching accuracy of any commodity face matcher. For time lapses larger than 10 years (the missing child is recovered after 10 or more years), the proposed age-progression module improves the rank-1 open-set identification accuracy of CosFace from 22.91 % to 25.04% 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.