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A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Finger Vein Recognition Algorithm Using Feature Block Fusion and Depth Neural Network

Authors : Cheng Chen, Zhendong Wu, Ping Li, Jianwu Zhang, Yani Wang, Hailong Li

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

Along with the development of biometric recognition, the technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies. The available finger vein recognition method is mainly based on template matching or whole feature recognition, suffering from light instability of the acquisition equipment which leads to low robustness. In order to strengthen the robustness, we adopt a finger vein recognition algorithm using Feature Block Fusion and Deep Belief Network (FBF-DBN), in which the nonlinear learning ability of deep neural network is used to recognize the features of finger veins. Meanwhile, we improve deep network input by using feature points set in vein images, sharply reducing the time in learning and detection, meeting the practical needs of biometric recognition specifically applied to embedded equipment. Experimental results showed that FBF-DBN algorithm presented better recognition performance and faster speed.

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Metadata
Title
A Finger Vein Recognition Algorithm Using Feature Block Fusion and Depth Neural Network
Authors
Cheng Chen
Zhendong Wu
Ping Li
Jianwu Zhang
Yani Wang
Hailong Li
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0356-1_60

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