Learning Local Direction Binary Code for 3D Palmprint

YANG Bing; MO Wen-bo; YAO Jin-liang

doi:10.12263/DZXB.20190473

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Learning Local Direction Binary Code for 3D Palmprint

PAPERS | 更新时间：2025-12-08

- Learning Local Direction Binary Code for 3D Palmprint
- ACTA ELECTRONICA SINICA Vol. 49, Issue 8, Pages: 1533-1540(2021)
- 作者机构：
  
  1.杭州电子科技大学计算机学院，浙江杭州 310018
  2.衢州职业技术学院信息工程学院，浙江衢州 324000
  3.浙江省脑机协同智能重点实验室，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20190473
  CLC： TP391.4;
- Received：29 April 2019，
  
  Revised：2021-02-02，
  
  Published：25 August 2021
- 稿件说明：
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杨冰,莫文博,姚金良.三维掌纹局部方向特征二进制编码研究[J].电子学报,2021,49(08):1533-1540.

YANG Bing,MO Wen-bo,YAO Jin-liang.Learning Local Direction Binary Code for 3D Palmprint[J].ACTA ELECTRONICA SINICA,2021,49(08):1533-1540.
杨冰,莫文博,姚金良.三维掌纹局部方向特征二进制编码研究[J].电子学报,2021,49(08):1533-1540. DOI： 10.12263/DZXB.20190473.

YANG Bing,MO Wen-bo,YAO Jin-liang.Learning Local Direction Binary Code for 3D Palmprint[J].ACTA ELECTRONICA SINICA,2021,49(08):1533-1540. DOI： 10.12263/DZXB.20190473.

摘要

三维掌纹能显著地减少应用过程中潜在的安全隐患，近年来吸引了越来越多的关注.然而，现有的三维掌纹识别方法大多借助人工设计的描述符来进行匹配，这往往需要先验知识.本文提出一种基于学习策略的局部方向特征二进制编码来完成三维掌纹识别.该方法利用形状指数来描述三维掌纹的局部几何特征，并且在形状指数图像上计算Gabor滤波器响应并将响应差值组合起来形成特征向量.提出利用哈希学习模型得到特征映射函数并将响应差值特征向量转换为方向特征二进制编码，并对方向特征二进制编码图采用分块策略形成特征直方图来进行匹配.在香港理工三维掌纹数据库上的实验结果表明，本文方法在识别率上要优于目前流行的其他三维掌纹识别方法，从而验证了本文方法的有效性.

Abstract

Three dimensional (3D) palmprint could efficiently reduce potential security risks in practical applications

and thus attracts a growing interest in recent years. However

most existing 3D palmprint recognition methods require hand-craft designed descriptors and strong prior knowledge are needed. In this paper

we propose a local direction binary code learning method for 3D palmprint recognition. We employ shape index representation to demonstrate the geometry characteristics of local regions in 3D palmprint data

and form Gabor filter response difference vector as the feature vectors by applying the Gabor filter on the shape index image. We utilize the hash learning model to learn feature mapping functions that can project these feature vectors into direction binary code

and further construct the block-wise histograms for matching. Experiments on Hong Kong Polytechnic University 3D palmprint database validate that our method outperforms existing state-of-the-art methods in terms of recognition accuracy

showing the effectiveness of our method.

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references

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