Abstract
We investigate whether it is possible to improve the performance of automated facial forensic sketch matching by learning from examples of facial forgetting over time. Forensic facial sketch recognition is a key capability for law enforcement, but remains an unsolved problem. It is extremely challenging because there are three distinct contributors to the domain gap between forensic sketches and photos: The well-studied sketch-photo modality gap, and the less studied gaps due to (i) the forgetting process of the eye-witness and (ii) their inability to elucidate their memory. In this paper, we address the memory problem head on y introducing a database of 400 forensic sketches created at different time-delays. Based on this database we build a model to reverse the forgetting process. Surprisingly, we show that it is possible to systematically “un-forget” facial details. Moreover, it is possible to apply this model to dramatically improve forensic sketch recognition in practice: we achieve the state of the art results when matching 195 benchmark forensic sketches against corresponding photos and a 10,030 mugshot database.
| Original language | English |
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| Title of host publication | Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 5571-5579 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-4673-8851-1 |
| ISBN (Print) | 978-1-4673-8852-8 |
| DOIs | |
| Publication status | Published - 12 Dec 2016 |
| Event | 29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States Duration: 26 Jun 2016 → 1 Jul 2016 http://cvpr2016.thecvf.com/ |
Conference
| Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition |
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| Abbreviated title | CVPR 2016 |
| Country/Territory | United States |
| City | Las Vegas |
| Period | 26/06/16 → 1/07/16 |
| Internet address |