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2020 | OriginalPaper | Buchkapitel

A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes

verfasst von: Haocong Zheng, Yongjian Hu, Beibei Liu, Guang Chen, Alex C. Kot

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2020

Verlag: Springer International Publishing

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Abstract

Existing deep learning-based finger-vein algorithms tend to use large-scale neural networks. From the perspective of computational complexity, this is not conducive to practical applications. Besides, in our opinion, finger-vein images often have relatively simple textures and are small in image size, it is not economical to use large-scale neural networks. Inspired by the increasing accuracy of lightweight neural networks on ImageNet, we introduce the lightweight neural network ShuffleNet V2 as a backbone to construct a basic pipeline for finger-vein verification. To customize the network for this application, we propose schemes to improve it from the aspects including data input, network structure, and loss function design. Experimental results on three public databases have exhibited the excellence of the proposed model.

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Metadaten
Titel
A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes
verfasst von
Haocong Zheng
Yongjian Hu
Beibei Liu
Guang Chen
Alex C. Kot
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-61609-0_59

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