An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification

Sulaiman, Dawlat Mustafa and Abdulazeez, Adnan Mohsin and Asaad Zebar, Dilovan and Zeebaree, Diyar Qader and Mostafa, Salama A. and Saleem Sadiq, Shereen (2022) An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification. Traitement du Signal, 39 (6). pp. 1-13. ISSN 1991-2002

Text J15753_aac300d0e453cca2bcb3c5f096e94385.pdf Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on the region-of-interest (ROI) learning instead of learning the whole feature domain. This technique makes the Deep Regional Attention Model learn more significant features with less time and computational resources than the regular deep learning model. For experimental validation, we used different finger vein imaging datasets that have been extracted and generated using our model. Original finger vein images, localized finger vein images (with no background), localized grayscale finger vein images (grayscale images with no background and projected finger vein lines), and localized colored finger vein images (colored images with no background and projected finger vein lines) are used to train and test our model, which gets better results than traditional deep learning and other methods

Item Type:	Article
Uncontrolled Keywords:	biometrics, finger vein identification, deep regional attention, recurrent and residual neural network
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T175-178 Industrial research. Research and development
Divisions:	Faculty of Applied Science and Technology > Department of Technology and Natural Resources
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	12 Jun 2023 07:10
Last Modified:	12 Jun 2023 07:10
URI:	http://eprints.uthm.edu.my/id/eprint/8778

Actions (login required)

View Item