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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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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 :
Ouyang, S.
Hospedales, T.M.
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

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