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DC Field | Value | Language |
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dc.contributor.author | Galea, Christian | |
dc.contributor.author | Farrugia, Reuben A. | |
dc.date.accessioned | 2017-11-21T08:32:28Z | |
dc.date.available | 2017-11-21T08:32:28Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Galea, C., & Farrugia, R. A. (2017). Forensic face photo-sketch recognition using a deep learning-based architecture. IEEE Signal Processing Letters, 24(11), 1586-1590. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/24012 | |
dc.description.abstract | Numerous methods that automatically identify subjects depicted in sketches as described by eyewitnesses have been implemented, but their performance often degrades when using real-world forensic sketches and extended galleries that mimic law enforcement mug-shot galleries. Moreover, little work has been done to apply deep learning for face photo-sketch recognition despite its success in numerous application domains including traditional face recognition. This is primarily due to the limited number of sketch images available, which are insufficient to robustly train large networks. This letter aims to tackle these issues with the following contributions: 1) a state-of-the-art model pre-trained for face photo recognition is tuned for face photo-sketch recognition by applying transfer learning, 2) a three-dimensional morphable model is used to synthesise new images and artificially expand the training data, allowing the network to prevent over-fitting and learn better features, 3) multiple synthetic sketches are also used in the testing stage to improve performance, and 4) fusion of the proposed method with a state-of-the-art algorithm is shown to further boost performance. An extensive evaluation of several popular and state-of-the-art algorithms is also performed using publicly available datasets, thereby serving as a benchmark for future algorithms. Compared to a leading method, the proposed framework is shown to reduce the error rate by 80.7% for viewed sketches and lowers the mean retrieval rank by 32.5% for real-world forensic sketches. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Human face recognition (Computer science) | en_GB |
dc.subject | Biometric identification -- Technological innovation | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Forensic face photo-sketch recognition using a deep learning-based architecture | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/LSP.2017.2749266 | |
dc.publication.title | IEEE Signal Processing Letters | en_GB |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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RA08025793.pdf | 506.11 kB | Adobe PDF | View/Open |
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