Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162363
Title: Using double attention for text tattoo localisation
Authors: Xu, Xingpeng
Prasad, Shitala
Cheng, Kuanhong
Kong, Adams Wai Kin
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Xu, 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/bme2.12071
Project: RG21/19‐(S)
Journal: IET Biometrics
Abstract: Text 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.
URI: https://hdl.handle.net/10356/162363
ISSN: 2047-4938
DOI: 10.1049/bme2.12071
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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