Vein pattern visualisation for biometric identification with cGAN on a New Zealand dataset.

Autor: Keivanmarz A; School of Computing, Unitec Institute of Technology, Auckland, New Zealand., Sharifzadeh H; School of Computing, Unitec Institute of Technology, Auckland, New Zealand. Electronic address: hsharifzadeh@unitec.ac.nz.
Jazyk: angličtina
Zdroj: Forensic science international [Forensic Sci Int] 2024 Jul; Vol. 360, pp. 112050. Date of Electronic Publication: 2024 May 10.
DOI: 10.1016/j.forsciint.2024.112050
Abstrakt: Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein disclosure, to enhance vein pattern detection. However, these methods still face limitations in reliability when compared to Near-Infrared (NIR) reference images. One of the biggest challenges of the studies is the limited number of available datasets that have synchronised colour and NIR images from body limbs. This paper introduces a new dataset comprising 602 pairs of synchronised NIR and RGB forearm images from a diverse population, ethically approved and collected in Auckland, New Zealand. Using this dataset, we also propose a conditional Generative Adversarial Networks (cGANs) model to translate RGB images into their NIR equivalents. Our evaluations focus on matching accuracy, vein length measurements, and contrast quality, demonstrating that the translated vein patterns closely resemble their NIR counterparts. This advancement offers promising implications for forensic identification techniques.
Competing Interests: Declaration of Competing Interest The authors, Ali Keivanmarz and Hamid Sharifzadeh, declare no conflict of interest regarding the publication of the paper titled “Vein Pattern Visualisation for Biometric Identification with cGAN on a New Zealand Dataset”.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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