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ForgetMeNot: Memory-aware forensic facial sketch matching

Ouyang, S., Hospedales, T.M., Song, Yi-Zhe and Li, X. (2017) ForgetMeNot: Memory-aware forensic facial sketch matching In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 27-30 Jun 2016, Las Vegas, NV, USA.

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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 the state of the art results when matching 195 benchmark forensic sketches against corresponding photos and a 10,030 mugshot database.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Ouyang, S.
Hospedales, T.M.
Song, Yi-Zhey.song@surrey.ac.uk
Li, X.
Date : February 2017
Funders : European Union's Horizon 2020, Royal Society
DOI : 10.1109/CVPR.2016.601
Uncontrolled Keywords : Forensics; Databases; Face recognition; Face; Benchmark testing; Facial features
Related URLs :
Additional Information : Printed proceedings published by Curran Associates Inc.
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 08:50
Last Modified : 12 Aug 2019 08:50
URI: http://epubs.surrey.ac.uk/id/eprint/852122

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