Multispectral Palm-vein Fusion for User Identification
- Authors
- Lee, J.; Kim, J.; Kim, D.; Ah, Lee S.; Hwan, Sung J.; Toh, K.-A.
- Issue Date
- 1-Jan-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Feature level fusion; Palm-vein recognition; Residual learning
- Citation
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- Journal Title
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30993
- DOI
- 10.1109/ICEIC57457.2023.10049882
- ISSN
- 0000-0000
- Abstract
- In this paper, we propose a system for user identification based on palm-veins extracted from multi-spectral images of the palm. Essentially, a feature level fusion is firstly conducted by stacking the preprocessed palm images from multiple image spectrums to increase the richness of information. Subsequently, a convolution neural network (CNN), which utilizes the residual learning with a linear bottleneck scheme, is adopted to learn the stacked features. The proposed system has been evaluated on a public multispectral palm database where a promising performance in terms of the identification accuracy has been observed. © 2023 IEEE.
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Collections - College of Engineering > Chemical Engineering Major > 1. Journal Articles
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