Budka, M., Ashraf, A.W.U., Neville, S., Mackrill, A and Bennett, M., 2021. Deep Multilabel CNN for Forensic Footwear Impression Descriptor Identification. Applied Soft Computing, 109 (September), 107496.
Full text available as:
|
PDF (OPEN ACCESS ARTICLE)
1-s2.0-S1568494621004191-main.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. 3MB | |
PDF (arXiv preprint)
2102.05090v1.pdf - Published Version Restricted to Repository staff only 19MB | ||
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.1016/j.asoc.2021.107496
Abstract
In recent years deep neural networks have become the workhorse of computer vision. In this paper, we employ a deep learning approach to classify footwear impression's features known as \emph{descriptors} for forensic use cases. Within this process, we develop and evaluate an effective technique for feeding downsampled greyscale impressions to a neural network pre-trained on data from a different domain. Our approach relies on learnable preprocessing layer paired with multiple interpolation methods used in parallel. We empirically show that this technique outperforms using a single type of interpolated image without learnable preprocessing, and can help to avoid the computational penalty related to using high resolution inputs, by making more efficient use of the low resolution inputs. We also investigate the effect of preserving the aspect ratio of the inputs, which leads to considerable boost in accuracy without increasing the computational budget with respect to squished rectangular images. Finally, we formulate a set of best practices for transfer learning with greyscale inputs, potentially widely applicable in computer vision tasks ranging from footwear impression classification to medical imaging.
Item Type: | Article |
---|---|
ISSN: | 1568-4946 |
Uncontrolled Keywords: | Deep learning; Forensics; Footwear impressions |
Group: | Faculty of Science & Technology |
ID Code: | 35475 |
Deposited By: | Symplectic RT2 |
Deposited On: | 12 May 2021 14:36 |
Last Modified: | 14 Mar 2022 14:27 |
Downloads
Downloads per month over past year
Repository Staff Only - |