基于近邻三值模式和协作表示的三维掌纹识别

doi:10.11772/j.issn.1001-9081.2018102124

计算机应用 ›› 2019, Vol. 39 ›› Issue (6): 1690-1695.DOI: 10.11772/j.issn.1001-9081.2018102124

基于近邻三值模式和协作表示的三维掌纹识别

刘玉珍¹, 蒋政权², 赵娜³

1. 辽宁工程技术大学电子与信息工程学院, 辽宁葫芦岛 125105;
2. 辽宁工程技术大学研究生院, 辽宁葫芦岛 125105;
3. 国网鞍山供电公司, 辽宁鞍山 114000

收稿日期:2018-10-22 修回日期:2019-01-16 发布日期:2019-06-17 出版日期:2019-06-10
通讯作者: 赵娜
作者简介:刘玉珍(1964-),女,吉林德惠人,教授,硕士,主要研究方向:信号检测;蒋政权(1995-),男,辽宁大连人,硕士,主要研究方向:遥感图像处理;赵娜(1994-),女,辽宁朝阳人,硕士研究生,主要研究方向:图像处理、模式识别。
基金资助:
辽宁省教育厅高等学校基本科研项目（LJ2017QL014）。

Three dimensional palmprint recognition based on neighbor ternary pattern and collaborative representation

LIU Yuzhen¹, JIANG Zhengquan², ZHAO Na³

1. School of Electronics and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China;
2. Graduate School, Liaoning Technical University, Huludao Liaoning 125105, China;
3. State Grid Anshan Electric Power Supply Company, Anshan Liaoning 114000, China

Received:2018-10-22 Revised:2019-01-16 Online:2019-06-17 Published:2019-06-10
Supported by:
This work is partially supported by the Basic Research Project of Liaoning Provincial Department of Education (LJ2017QL014).

摘要/Abstract

摘要： 针对二维掌纹图像存在易伪造、抗噪能力差的问题，提出一种基于近邻三值模式（NTP）和协作表示的三维掌纹识别方法。首先，利用形状指数把三维掌纹的表面几何信息映射成二维数据，以弥补常用均值或高斯曲率映射无法精确描述三维掌纹特征的缺陷；其次，对形状指数图作分块处理，利用近邻三值模式提取分块形状指数图的纹理特征；最后，利用协作表示的方法进行特征分类。在三维掌纹库上和经典算法进行的对比实验中，该方法的识别率为99.52%，识别时长为0.6738 s，优于其他算法；在识别率方面，与经典的局部二值模式（LBP）、局部三值模式（LLTP）、CompCode、均值曲率图（MCI）法相比分别提高了7.77%、6.02%、5.12%和3.97%；在识别时间方面，与Homotopy、对偶增广拉格朗日法（DALM）、SpaRSA方法相比分别降低了6.7 s、15.9 s和61 s。实验结果表明，所提算法具有良好的特征提取和分类能力，能够有效地提高识别精度并减少识别时间。

关键词: 三维掌纹, 形状指数, 局部三值模式, 纹理特征, 协作表示

Abstract: Concerning the problem that two Dimensional (2D) palmprint images are eaasily to be forged and affected by noise, a three Dimensional (3D) palmprint recognition method based on Neighbor Ternary Pattern (NTP) and collaborative representation was proposed. Firstly, a shape index was used to map the surface geometric information of 3D palmprint into 2D data, avoiding the inaccurate description of 3D palmprint features by common mean value or Gaussian curvature mapping. Secondly, the shape index image was divided into several blocks, and NTP algorithm was used to extract texture features of divided shape index images. Finally, collaborative representation was used to classify the features. Experiments on 3D palmprint base show that compared with the classical algorithms, the proposed method has the best recognition effect with recognition rate of 99.52% and recognition time of 0.6738 s. The proposed method improves the recognition rate by 7.77%, 6.02%, 5.12% and 3.97% respectively compared to Local Binary Pattern (LBP), Local Ternary Pattern (LTP), CompCode and Mean Curvature Image (MCI) method; the proposed method reduces the recognition time by 6.7 s, 15.9 s and 61 s compared to Homotopy, Dual Augmented Lagrangian Algorithm (DALM) and SpaRSA method. The experimental results show that the proposed algorithm has good feature extraction and classification ability, which can effectively improve the recognition accuracy and reduce the recognition time.

Key words: three dimensional palmprint, shape index, local ternary pattern, texture feature, collaborative representation

中图分类号:

TP391.4

刘玉珍, 蒋政权, 赵娜. 基于近邻三值模式和协作表示的三维掌纹识别[J]. 计算机应用, 2019, 39(6): 1690-1695.

LIU Yuzhen, JIANG Zhengquan, ZHAO Na. Three dimensional palmprint recognition based on neighbor ternary pattern and collaborative representation[J]. Journal of Computer Applications, 2019, 39(6): 1690-1695.

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基于近邻三值模式和协作表示的三维掌纹识别

Three dimensional palmprint recognition based on neighbor ternary pattern and collaborative representation

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