LAWNet: A Lightweight Attention-Based Deep Learning Model for Wrist Vein Verification in Smartphones Using RGB Images

Nguyen, TV; Horng, SJ; Vu, DT; Chen, H; Li, T

LAWNet: A Lightweight Attention-Based Deep Learning Model for Wrist Vein Verification in Smartphones Using RGB Images

Nguyen, TV Horng, SJ Vu, DT Chen, H Li, T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2023, 72, pp. 1-10
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Nguyen, TV
dc.contributor.author	Horng, SJ
dc.contributor.author	Vu, DT
dc.contributor.author	Chen, H
dc.contributor.author	Li, T
dc.date.accessioned	2024-05-11T20:49:33Z
dc.date.available	2024-05-11T20:49:33Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2023, 72, pp. 1-10
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/178838
dc.description.abstract	The wrist vein is one robust and reliable biometric for research and applications in automatic human verification; however, the existing wrist vein recognition models were heavy and ineffective in deploying on smartphones. Also, smartphones were required to integrate near-infrared (NIR) sensors to capture wrist vein images. This article proposes a novel lightweight attention-based deep learning model for wrist vein verification in regular smartphones (LAWNet) using red-green-blue (RGB) images. We first used a saturation channel converted from an RGB image as LAWNet's input rather than a NIR image, as in previous research. Second, we presented a new wrist region of interest (ROI) extraction method. This step was usually neglected in previous research on contact wrist NIR databases; however, a correct wrist ROI can improve the final recognition performance in a contactless environment. Third, we proposed a novel LAWNet model for wrist vein extractors. Our model was formed by one convolutional block, four improved sandglass blocks, followed by a mixed pooling coordinate attention (MPCA) module, and completed with one parallel spatial pyramid pooling (PSPP) block. The experiment results showed that the proposed model has fewer parameters but achieved the best performance among compared models on our self-collected RGB wrist vein database with an equal error rate (EER) of 1.80%; furthermore, the speed of the complete wrist vein recognition system deployed on smartphones is extremely fast at 60 frames/s, which is acceptable in practice.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2023.3328702
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0299 Other Physical Sciences, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	LAWNet: A Lightweight Attention-Based Deep Learning Model for Wrist Vein Verification in Smartphones Using RGB Images
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	0299 Other Physical Sciences
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	in_progress	*
dc.date.updated	2024-05-11T20:49:30Z
pubs.publication-status	Published
pubs.volume	72

Abstract:

The wrist vein is one robust and reliable biometric for research and applications in automatic human verification; however, the existing wrist vein recognition models were heavy and ineffective in deploying on smartphones. Also, smartphones were required to integrate near-infrared (NIR) sensors to capture wrist vein images. This article proposes a novel lightweight attention-based deep learning model for wrist vein verification in regular smartphones (LAWNet) using red-green-blue (RGB) images. We first used a saturation channel converted from an RGB image as LAWNet's input rather than a NIR image, as in previous research. Second, we presented a new wrist region of interest (ROI) extraction method. This step was usually neglected in previous research on contact wrist NIR databases; however, a correct wrist ROI can improve the final recognition performance in a contactless environment. Third, we proposed a novel LAWNet model for wrist vein extractors. Our model was formed by one convolutional block, four improved sandglass blocks, followed by a mixed pooling coordinate attention (MPCA) module, and completed with one parallel spatial pyramid pooling (PSPP) block. The experiment results showed that the proposed model has fewer parameters but achieved the best performance among compared models on our self-collected RGB wrist vein database with an equal error rate (EER) of 1.80%; furthermore, the speed of the complete wrist vein recognition system deployed on smartphones is extremely fast at 60 frames/s, which is acceptable in practice.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178838