北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1807-1816.doi: 10.13700/j.bh.1001-5965.2020.0309

• 论文 • 上一篇    

基于U-Net的掌纹图像增强与ROI提取

陆展鸿1, 单鲁斌2, 苏立循2, 焦雨欣1, 王家骅1, 王海霞3   

  1. 1. 杭州海康威视数字技术股份有限公司, 杭州 310051;
    2. 浙江工业大学 信息工程学院, 杭州 310023;
    3. 浙江工业大学 计算机科学与技术学院, 杭州 310023
  • 收稿日期:2020-07-01 发布日期:2020-09-22
  • 通讯作者: 王海霞 E-mail:hxwang@zjut.edu.cn
  • 作者简介:陆展鸿 男,学士。主要研究方向:图像识别技术;王海霞 女,博士,副教授,博士生导师。主要研究方向:精密测量与图像处理。
  • 基金资助:
    浙江省重点研发计划(2019C01007)

Palmprint enhancement and ROI extraction based on U-Net

LU Zhanhong1, SHAN Lubin2, SU Lixun2, JIAO Yuxin1, WANG Jiahua1, WANG Haixia3   

  1. 1. Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China;
    2. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China;
    3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • Received:2020-07-01 Published:2020-09-22
  • Supported by:
    Zhejiang Povincial Key Research and Development Program of China (2019C01007)

摘要: 掌纹因稳定性、唯一性、难复制性及易获取等特点,是极具应用潜力的生物识别特征。针对掌纹识别中,获取掌纹感兴趣区域(ROI)与增强掌纹信息普遍存在的时间成本大,方法之间有依赖关系等问题,提出了一种基于U-Net神经网络结构的掌纹图像预处理方法。通过香港理工大学掌纹库进行实验对比,结果表明,所提方法能消除预处理方法之间的相互影响,实现对掌纹图像的去噪与增强处理,并能快速、高精度提取感兴趣区域。

关键词: U-Net, 感兴趣区域(ROI)增强, 深度学习, 感兴趣区域(ROI)提取, 掌纹

Abstract: Palmprint is a biometric feature with great application potential due to its stability,uniqueness,hardness in copying,and easy access.In the palmprint recognition,palmprint Region of Interest(ROI) acquisition and palmprint enhancement usually have the problems of high time cost and high dependency between methods.This paper proposes a palmprint preprocessing method based on U-Net neural network structure.The experiments are carried out on palmprint database from Hong Kong Polytechnic University.The results show that the proposed method can eliminate the mutual influence between the preprocessing methods.Both denoising and enhancement of palmprint images are realized,and the region of interest can be extracted quickly and accurately; plamprint

Key words: U-Net, Region of Interest (ROI) enhancement, deep learning, Region of Interest (ROI) extraction, plamprint

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