Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model

Marlina, Yakno and Junita, Mohamad-Saleh and Mohd Zamri, Ibrahim (2022) Dorsal hand vein segmentation using Vein-Generative Adversarial Network (V-GAN) Model. In: Lecture Notes in Electrical Engineering; 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021 , 5-6 April 2021 , Virtual, Online. pp. 585-591., 829 LNEE (272139). ISSN 1876-1100 ISBN 978-981168128-8

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Difficulty in achieving intravenous access in some patients is a clinical problem due to extreme age, body size, and chronic disease patients. In biometric identification, hand vein patterns are useful when other external identifiers are more prone to be damaged or forged. To overcome these problems, near-infrared dorsal hand vein images are captured and segmented for vein extraction. However, the segmentation process becomes more challenging when the infrared im- ages suffer from extremely low contrast and distortion, indirectly affecting the segmentation process. Therefore, this work presents a method for generating an accurate map of dorsal hand vein patterns using deep learning Vein-Generative Adversarial Networks (V-GAN). The performance of V-GAN is measured in terms of accuracy, Area under Curve (AUC), F1-score, sensitivity, specificity, and dice-coefficient.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Generative adversarial network; Hand vein segmentation
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: College of Engineering
Faculty of Electrical and Electronic Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 13 Dec 2023 03:34
Last Modified: 13 Dec 2023 03:34
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