NDLI: Finger vein verification using a Siamese CNN

Content Provider	IET Digital Library
Author	Tang, Su Zhou, Shan Kang, Wenxiong Wu, Qiuxia Deng, Feiqi
Abstract	Finger vein verification has received more attention recently due to its unique advantages. However, most existing algorithms rely on handcrafted features, making them less robust to finger rotation and offsets. To alleviate these problems, the authors propose a novel method to extract more discriminative features from finger vein images. First, facing the issue of insufficient training data, they adopt a heavy image augmentation strategy and develop a pretrained-weights based convolutional neural network (CNN). Second, focusing on the characteristics of finger vein verification, they construct a Siamese structure combining with a modified contrastive loss function for training the above CNN, which effectively improves the network's performance. Finally, considering the feasibility of deploying the above CNN on embedded devices, they construct a lightweight CNN with depthwise separable convolution and adopt a knowledge distillation method to learn the knowledge from the pretrained-weights based CNN, which makes it small but effective. The experimental results show that the size of the lightweight CNN shrinks to 1/6th of the pretrained-weights based CNN, while its equal error rates achieved in the MMCBNU_6000, FV-USM and SDUMLA-HMT datasets are 0.08, 0.11 and 0.75% respectively, which nearly stays the same with the pretrained-weights based CNN and surpasses state-of-the-art methods.
Starting Page	306
Ending Page	315
Page Count	10
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 5, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5245
Journal	IET Biometrics
Publisher Date	2019-03-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Neural Nets Depthwise Separable Convolution Discriminative Feature Extraction Embedded Device Equal Error Rates Feature Extraction Finger Offsets Finger Rotation Finger Vein Image Finger Vein Verification FV-USM Datasets Handcrafted Feature Heavy Image Augmentation Strategy Image Quality Deterioration Image Recognition Insufficient Training Data Knowledge Distillation Method Learning in AI Lightweight CNN Metric Learning MMCBNU_6000 Datasets Modified Contrastive Loss Function Neural Computing Technique Pretrained-Weight Based CNN Pretrained-Weight Based Convolutional Neural Network SDUMLA-HMT Datasets Siamese CNN Siamese Structure Vein Recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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