ForgetMeNot: Memory-Aware Forensic Facial Sketch Matching
Shuxin Ouyang, Timothy M. Hospedales, Yi-Zhe Song, Xueming Li
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 by 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 state of the art results when matching 195 benchmark forensic sketches against corresponding photos and a 10,030 mugshot database.
BibTeX
@inproceedings{cvpr2016_forgetmenotmemor,
title = {ForgetMeNot: Memory-Aware Forensic Facial Sketch Matching},
author = {Shuxin Ouyang and Timothy M. Hospedales and Yi-Zhe Song and Xueming Li},
booktitle = {CVPR 2016},
year = {2016}
}