Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.82471-82484
Main Authors: Wu, Wei, Wang, Qiang, Yu, Siquan, Luo, Qiong, Lin, Sen, Han, Zhi, Tang, Yandong
Format: Article
Language:English
Subjects:
Biometric recognition systems
biometrics
Blurring
Deep learning
denoise
Face recognition
Feature extraction
Image classification
Image databases
Image rotation
Machine learning
Noise reduction
Optical imaging
Optical scattering
palm vein recognition
Resnet
Veins
wavelet decomposition
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve the hygienic problem but face the image rotation, position translation, and scale transformation which makes classification difficult especially in large-scale databases. To address these problems, we develop a wavelet denoising ResNet, which consists of two models: the wavelet denoising (WD) model and the squeeze-and-excitation ResNet18 (SER) model. The WD model focuses on removing noise from skin scattering and optical blurring from palm vein images. The WD model enhances the low-frequency feature into a deep learning feature by residual learning technology. This strategy increases the weight of an effective handcrafted feature in the deep learning network. The SER model overcomes rotation, position translation, and scale transformation by selectively emphasizing classification features and weakening less useful features. To train and verify the network, an inside box palm vein image database and an outside box palm vein image database are set up. The Tongji contactless palm vein image database was also employed in the experiments. The validity and superiority of our network are verified in a series of experiments.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3086811