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https://www.um.edu.mt/library/oar/handle/123456789/27599
Title: | Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning |
Authors: | Galea, Christian Farrugia, Reuben A. |
Keywords: | Convolutions (Mathematics) Machine learning Artificial intelligence |
Issue Date: | 2018 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Citation: | Galea, C., & Farrugia, R. A. (2018). Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning. IEEE Transactions on Information Forensics and Security, 13(6), 1421-1431. |
Abstract: | Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and stateof-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/27599 |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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Matching_Software_Generated_Sketches_to_Face_2018.pdf | 2 MB | Adobe PDF | View/Open |
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