Mode control of electrically injected semiconductor laser with parity-tim...
Eight-channel hybrid-integrated laser array with 100 GHz channel spacing
Numerical simulation analysis of effect of energy band alignment and func...
Observation of nonreciprocal magnetophonon effect in nonencapsulated few-...
Electric Field Tuning of Interlayer Coupling in Noncentrosymmetric 3R-MoS...
Even-odd-dependent optical transitions of zigzag monolayer black phosphor...
Role of interfacial 2D graphene in high performance 3D graphene/germanium...
All-MBE grown InAs/GaAs quantum dot lasers with thin Ge buffer layer on S...
Effect of Processing Technique Factors on Structure and Photophysical Pro...
"Fast" Plasmons Propagating in Graphene Plasmonic Waveguides with Negativ...
官方微信
友情鏈接

Adaptive Learning Gabor Filter for Finger-Vein Recognition

2020-11-19

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

Ning, Xin                  0000-0001-7897-1673

ISSN: 2169-3536

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

semi@semi.ac.cn

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

打杭州麻将的app有哪些 微乐吉林长春麻将官方下载 天津快乐十分0629049 11选5任8每天赚300计划 星悦麻将丽江卡心五 老友东北麻将玩法技巧 电子app制作 陕西快乐十分钟开奖结果 广东11选5最容易中奖 三期内必开一期王中王 心悦吉林麻将完整版 麻将绝技教学视频 开元棋牌是机器人在控制吗 湖南赛车app官方下载 广东26选5奖金计算器 河南11选5规则 六肖中特期期准