Enhance decoding of pre-movement EEG patterns for brain-computer interfaces
The Characteristics and Locking Process of Nonlinear MEMS Gyroscopes
Evaluation of polarization field in InGaN/GaN multiple quantum well struc...
Growth Control of High-Performance InAs/GaSb Type-II Superlattices via Op...
High-Performance Germanium Waveguide Photodetectors on Silicon*
Nanoscale thermal transport across an GaAs/AlGaAs heterostructure interface
Investigation of modulation transfer function in InGaAs photodetector sma...
Seed-mediated growth of heterostructured Cu1.94S-MS (M = Zn, Cd, Mn) and ...
High-performance phosphorene electromechanical actuators
Recent Advances of Two-Dimensional Nanomaterials for Electrochemical Capa...
官方微信
友情鏈接

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



關于我們
下載視頻觀看
聯系方式
通信地址

北京市海淀區清華東路甲35號 北京912信箱 (100083)

電話

010-82304210/010-82305052(傳真)

E-mail

[email protected]

交通地圖
版權所有 中國科學院半導體研究所

備案號:京ICP備05085259號 京公網安備110402500052 中國科學院半導體所聲明

在藏区为藏民照相赚钱吗 河南省22选5预测号 上海快三安卓版下载 乌鲁木齐股指期货配资 pk10最精准计划 甘肃十一选五开奖查询 购买广西11选五平台分享 福建高频十一选五下载 体彩11选五中奖助手 保定股指期货配资 快乐12号码查询