High-Quality Hexagonal Nonlayered CdS Nanoplatelets for Low-Threshold Whi...
Low insertion loss silicon-based spatial light modulator with high reflec...
Effect of Nanodisks at Different Positions on the Fano Resonance of Graph...
Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling ...
High-resolution and fast-response optical waveguide temperature sensor us...
Optical phase matching of high-order azimuthal WGM in a water droplet res...
Interface-driven unusual anomalous Hall effect in MnxGa/Pt bilayers
A discussion on mass resolution in secondary ion mass spectrometry
Improvement of beam quality for fiber-coupled diode-side-pumped Nd:YAG ro...
Applications of Fiber Optics Sensors in Seismology
官方微信
友情链接

Adaptive Learning Gabor Filter for Finger-Vein Recognition

2019-12-05

 

Author(s): Zhang, YK (Zhang, Yakun); Li, WJ (Li, Weijun); Zhang, LP (Zhang, Liping); Ning, X (Ning, Xin); Sun, LJ (Sun, Linjun); Lu, YX (Lu, Yaxuan)

Source: IEEE ACCESS Volume: 7 Pages: 159821-159830 DOI: 10.1109/ACCESS.2019.2950698 Published: 2019

Abstract: Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter $\theta $ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter $\sigma $ of Gabor filter has a certain relation with $\lambda $ , and the parameter $\lambda $ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification.

Accession Number: WOS:000497167600080

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Zhang, Yakun                  0000-0001-5829-1371

Full Text: https://ieeexplore.ieee.org/document/8888260



关于我们
下载视频观看
联系方式
通信地址

北京市海淀区清华东路甲35号 北京912信箱 (100083)

电话

010-82304210/010-82305052(传真)

E-mail

semi@semi.ac.cn

交通地图
版权所有 ? 中国科学院半导体研究所

备案号:京ICP备05085259号 京公网安备110402500052 中国科学院半导体所声明