Debayan Deb, Divyansh Aggarwal, Anil K. Jain
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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Facial Recognition and Modelling | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |
| Face Reconstruction | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |
| Face Recognition | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |
| 3D | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |
| 3D Face Modelling | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |
| 3D Face Reconstruction | UHDB31 | Rank-1 | 94.5 | Wang et al. [5] |