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.

参考文献

[1] AGGITHAYA V K, ZHANG D, LUO N. A multimodal biometric authentication system based on 2D and 3D palmprint features[C]//Proceedings of the SPIE 6944- the International Society for Optical Engineering, Biometric Technology for Human Identification V. Bellingham:SPIE, 2008:69440C.
[2] ZHANG D, LU G M, LI W, et al. Three dimensional palmprint recognition using structured light imaging[C]//Proceedings of the 2008 IEEE Second International Conference on Biometrics:Theory, Applications and Systems. Piscataway, NJ:IEEE, 2008:1-6.
[3] BAI X, HUANG S, GAO N, et al. Person identification by using 3D palmprint data[C]//Proceedings of the SPIE 10025, Advanced Sensor Systems and Applications VⅡ. Bellingham:SPIE, 2016:100250Q.
[4] BAI X, GAO N, ZHANG Z, et al. 3D palmprint identification combining blocked ST and PCA[J]. Pattern Recognition Letters, 2017, 100:89-95.
[5] 林国军,解梅.一种鲁棒协作表示的人脸识别算法[J].计算机应用研究,2014,31(8):2520-2522,2531.(LIN G J, XIE M. Face recognition algorithm of robust collaborative representation[J]. Application Research of Computers, 2014, 31(8):2520-2522, 2531.)
[6] GUO X M, ZHOU W D, ZHANG Y L. Collaborative representation with HM-LBP features for palmprint recognition[J]. Machine Vision and Applications, 2017, 28(3/4):283-291.
[7] ZHANG D, LU G M, LI W, et al. Palmprint recognition using 3-D information[J]. IEEE Transactions on Systems, Man and Cybernetics, Part C:Applications and Reviews, 2009, 39(5):505-519.
[8] 杨冰,王小华,杨鑫.基于局部纹理特征的三维掌纹识别研究[J].光电工程,2014,41(12):53-59.(YANG B, WANG X H, YANG X. 3D Palmprint recognition based on local texture feature sets[J]. Opto-Electronic Engineering, 2014, 41(12):53-59.)
[9] 姚骋天,夏哲雷.一种改进的局部三值模式的人脸识别方法[J].中国计量学院学报,2016,27(1):68-72.(YAO C T, XIA Z L. Face recognition based on improved local ternary patterns[J]. Journal of China University of Metrology, 2016,27(1):68-72.)
[10] 刘海军.局部方向三值模式纹理描述子[J].计算机工程与设计,2015,36(5):1260-1264,1303.(LIU H J. Texture descriptor of local directional ternary patterns[J]. Computer Engineering and Design, 2015, 36(5):1260-1264, 1303.)
[11] 林森,苑玮琦,吴微,等.基于离散余弦变换和主线分块能量的模糊掌纹识别[J].光电子·激光,2012,23(11):2200-2206.(LIN S, YUAN W Q, WU W, et al. Blurred palmprint recognition based on DCT and block enrgy of principal lines[J].Journal of Optoelectronics·Laser, 2012, 23(11):2200-2206.)
[12] 李善广.基于分块的掌纹识别算法研究[D].哈尔滨:哈尔滨工业大学,2013:18-23.(LI S G. Research on modular palmprint recognition algorithms[D]. Harbin:Harbin Institute of Technology, 2013:18-23.)
[13] 芦鑫.基于稀疏表示的遮挡人脸识别算法的研究与实现[D].北京:华北电力大学,2016:7-16.(LU X. Research and implementation of occluded face recognition based on sparse representation[D]. Beijing:North China Electric Power University, 2016:7-16.)
[14] ZHANG L, YANG M, FENG X C. Sparse representation or collaborative representation:which helps face recognition?[C]//Proceedings of the 2011 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2011:471-478.
[15] 王文龙,金炜, 谢芸,等.采用均匀局部二元模式及稀疏表示的掌纹识别[J].光电工程,2014,41(12):60-65.(WANG W L, JIN W, XIE Y, et al. Palmprint recognition using uniform local binary patterns and sparse representation[J].Opto-Electronic Engineering, 2014, 41(12):60-65.)
[16] 张宏星,邹刚,赵键,等.基于Gabor特征与协同表示的人脸识别算法[J].计算机工程与设计,2014,35(2):666-670,676.(ZHANG H X, ZOU G, ZHAO J, et al. Face recognition algorithm based on Gabor feature and collaborative representation[J]. Computer Engineering and Design, 2014,35(2):666-670, 676.)
[17] ZHANG L, LI L D, YANG A Q, et al. Towards contactless palmprint recognition:a novel device, a new benchmark, and a collaborative representation based identification approach[J]. Pattern Recognition, 2017, 69:199-212.
[18] 曹忠赞.基于2D和3D掌纹图像方向特征融合的掌纹识别研究[D].秦皇岛:燕山大学,2013:13-17.(CAO Z Z. Study on palmprint recognition based on 2D and 3D palmprint image orientation features fusion[D]. Qinhuangdao:Yashan University, 2013:13-17.)
[19] 李春燕,卢光明,黎伟.基于曲面曲率和RLDA 3D掌纹识别方法[J].中国图象图形学报,2011,16(5):807-812.(LI C Y, LU G M, LI W. 3D palmprint recognition based on surface curvature and RLDA[J]. Journal of Image and Graphics, 2011, 16(5):807-812.)
[20] ZHANG L, SHEN Y, LI H Y, et al. 3D palmprint identification using block-wise features and collaborative representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8):1730-1736.

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	蒋考林, 白玮, 张磊, 陈军, 潘志松, 郭世泽. 基于多通道图像深度学习的恶意代码检测[J]. 计算机应用, 2021, 41(4): 1142-1147.
[2]	余英东, 杨怡, 林澜. 结合纹理特征分析的图像风格转换网络[J]. 计算机应用, 2020, 40(3): 638-644.
[3]	郎大鹏, 丁巍, 姜昊辰, 陈志远. 基于多特征融合的恶意代码分类算法[J]. 计算机应用, 2019, 39(8): 2333-2338.
[4]	翁理国, 刘万安, 施必成, 夏旻. 基于多维多粒度级联森林的高原地区云雪分类[J]. 计算机应用, 2018, 38(8): 2218-2223.
[5]	李争名, 杨南粤, 岑健. 基于原子Fisher判别准则约束字典学习算法[J]. 计算机应用, 2017, 37(6): 1716-1721.
[6]	邹玮刚, 周志辉, 王洋. 基于非降采样轮廓波变换的图像修复算法[J]. 计算机应用, 2017, 37(2): 553-558.
[7]	董林佳, 强彦, 赵涓涓, 原杰, 赵文婷. 基于三维形状指数的肺结节自动检测方法[J]. 计算机应用, 2017, 37(11): 3182-3187.
[8]	刘珏先, 滕奇志, 王正勇, 何小海. 基于协同表示的多特征融合岩石分类[J]. 计算机应用, 2016, 36(3): 854-858.
[9]	谢佩, 吴小俊. 基于Shearlet变换和均匀局部二值模式特征的协作表示人脸识别算法[J]. 计算机应用, 2015, 35(7): 2056-2061.
[10]	谢永华, 徐赵飞, 范文晓. 基于高斯尺度空间粗糙度描述子的花粉图像分类识别[J]. 计算机应用, 2015, 35(7): 2039-2042.
[11]	马强, 王文伟. 融合显著性因子的行人纹理提取[J]. 计算机应用, 2015, 35(11): 3293-3296.
[12]	黄微傅利琴王琛. 基于梯度域的保纹理图像阴影去除算法[J]. 计算机应用, 2013, 33(08): 2317-2319.
[13]	张宁刘文萍. 基于克隆选择算法和K近邻的植物叶片识别方法[J]. 计算机应用, 2013, 33(07): 2009-2013.
[14]	苏宝莉. 基于协作表示和模糊渐进最大边界嵌入的特征抽取方法[J]. 计算机应用, 2013, 33(06): 1677-1681.
[15]	李睿刘昌旭年福忠. 基于自适应背景的多特征融合目标跟踪[J]. 计算机应用, 2013, 33(03): 651-655.