Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162363
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dc.contributor.authorXu, Xingpengen_US
dc.contributor.authorPrasad, Shitalaen_US
dc.contributor.authorCheng, Kuanhongen_US
dc.contributor.authorKong, Adams Wai Kinen_US
dc.date.accessioned2022-10-17T03:05:09Z-
dc.date.available2022-10-17T03:05:09Z-
dc.date.issued2022-
dc.identifier.citationXu, X., Prasad, S., Cheng, K. & Kong, A. W. K. (2022). Using double attention for text tattoo localisation. IET Biometrics, 11(3), 199-214. https://dx.doi.org/10.1049/bme2.12071en_US
dc.identifier.issn2047-4938en_US
dc.identifier.urihttps://hdl.handle.net/10356/162363-
dc.description.abstractText tattoos contain rich information about an individual for forensic investigation. To extract this information, text tattoo localisation is the first and essential step. Previous tattoo studies applied existing object detectors to detect general tattoos, but none of them considered text tattoo localisation and they neglect the prior knowledge that text tattoos are usually inside or nearby larger tattoos and appear only on human skin. To use this prior knowledge, a prior knowledge-based attention mechanism (PKAM) and a network named Text Tattoo Localisation Network based on Double Attention (TTLN-DA) are proposed. In addition to TTLN-DA, two variants of TTLN-DA are designed to study the effectiveness of different prior knowledge. For this study, NTU Tattoo V2, the largest tattoo dataset and NTU Text Tattoo V1, the largest text tattoo dataset are established. To examine the importance of the prior knowledge and the effectiveness of the proposed attention mechanism and the networks, TTLN-DA and its variants are compared with state-of-the-art object detectors and text detectors. The experimental results indicate that the prior knowledge is vital for text tattoo localisation; The PKAM contributes significantly to the performance and TTLN-DA outperforms the state-of-the-art object detectors and scene text detectors.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationRG21/19‐(S)en_US
dc.relation.ispartofIET Biometricsen_US
dc.rights© 2022The Authors. IET Biometrics published by John Wiley& Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivsLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleUsing double attention for text tattoo localisationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1049/bme2.12071-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85127616504-
dc.identifier.issue3en_US
dc.identifier.volume11en_US
dc.identifier.spage199en_US
dc.identifier.epage214en_US
dc.subject.keywordsAttention Mechanismen_US
dc.subject.keywordsTattoo Localisationen_US
dc.description.acknowledgementThis work is partially supported by the Ministry of Education, Singapore through Academic Research Fund Tier 1, RG21/19‐(S).en_US
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