Palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain

doi:10.11772/j.issn.1001-9081.2018010183

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (8): 2205-2210.DOI: 10.11772/j.issn.1001-9081.2018010183

Previous Articles Next Articles

Palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain

LI Xinchun¹, CAO Zhiqiang², LIN Sen¹, ZHANG Chunhua²

1. School of Electronics and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China;
2. Graduate School, Liaoning Technical University, Huludao Liaoning 125105, China

Received:2018-01-22 Revised:2018-03-11 Online:2018-08-10 Published:2018-08-11
Supported by:
This work is partially supported by the Scientific Research General Project of Liaoning Provincial Department of Education (L2014132); the Natural Science Foundation of Liaoning Province (2015020100).

掌纹掌脉图像超小波域融合识别算法

李新春¹, 曹志强², 林森¹, 张春华²

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

通讯作者: 曹志强
作者简介:李新春(1963-),男,辽宁喀左人,高级工程师,主要研究方向:无线传感器网络、数字图像处理;曹志强(1988-),男,河南驻马店人,硕士研究生,主要研究方向:图像处理、模式识别;林森(1980-),男,辽宁沈阳人,讲师,博士,主要研究方向:图像处理、模式识别;张春华(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:
辽宁省教育厅科学研究一般项目（L2014132）；辽宁省自然科学基金面上项目（2015020100）。

Abstract

Abstract: Single biometric identification technology can be easily affected by various external factors, thus the recognition rate and stability are poor. A palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain, namely NSCT-NBP, was proposed. Firstly, palmprint and palmvein images were decomposed by using Non-Subsampled Contourlet Transform (NSCT), then the obtained low-frequency and high-frequency sub-images were respectively merged by using the regional energy and image self-similarity principle. Secondly, the texture features were extracted from the fused images by using Neighbor based Binary Pattern (NBP), thus the eigenvector was got. Finally, the similarity of the fused images was calculated by Hamming distance of the feature vectors, to get Equal Error Rate (EER). The experiments were conducted on PolyU and a self-built database, the experimental results show that the lowest EER of NSCT-NBP algorithm were 0.72% and 0.96%, the identification time were only 0.0530 s and 0.0871 s. Compared with the current best palmprint-palmvein fusion method based on wavelet transform and Gabor filter, the EER of the two databases were reduced by 4% and 36.8%, respectively. The NSCT-NBP algorithm can effectively fuse the texture features of the palmprint-palmvein images and has good recognition performance. The fusion of palmprint-palmvein features can enhance the security of the recognition system.

Key words: image processing, multi-biometrics, Non-Subsampled Contourlet Transform (NSCT), Neighbor based Binary Pattern (NBP), Hamming distance

摘要： 针对单一生物特征识别技术易受外界各种因素影响，识别率和稳定性有待提高的问题，提出一种掌纹掌脉图像超小波域融合识别算法NSCT-NBP。首先，对掌纹掌脉图像利用非下采样Contourlet变换（NSCT）进行分解，将得到的低频和高频子图像分别利用区域能量和图像自相似原理进行融合；然后，对融合后的图像利用近邻二值模式（NBP）提取纹理特征，获得特征向量；最后，通过计算特征向量间的汉明距离比较融合图像间的近似程度来计算等误率（EER）。在PloyU图库及自建图库上进行实验，结果表明，NSCT-NBP算法可获得最低的EER，分别为0.72%和0.96%，识别时间仅为0.0530 s和0.0871 s，与当前最优的基于小波变换和Gabor滤波器的掌纹掌脉融合方法相比，在两个图库上EER分别降低了4%和36.8%。NSCT-NBP算法能够有效融合掌纹掌脉图像的纹理特征，具有良好的识别性能，并且掌纹掌脉特征的融合增强了识别系统的安全性。

关键词: 图像处理, 多生物特征识别, 非下采样Contourlet变换, 近邻二值模式, 汉明距离

CLC Number:

TP391.4

LI Xinchun, CAO Zhiqiang, LIN Sen, ZHANG Chunhua. Palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain[J]. Journal of Computer Applications, 2018, 38(8): 2205-2210.

李新春, 曹志强, 林森, 张春华. 掌纹掌脉图像超小波域融合识别算法[J]. 计算机应用, 2018, 38(8): 2205-2210.

References

[1] 李雪娇.基于手部特征的多模态生物识别技术研究[D]. 哈尔滨:哈尔滨理工大学,2016:1-5. (LI X J. Based on the biological characteristic of hand multi-modal technology research[D]. Harbin:Harbin University of Science and Technology, 2016:1-5.)
[2] 王以沙.基于手部心电信号的身份识别研究[D]. 天津:天津理工大学,2016:1-3. (WANG Y S. Research on human identification based on palm lead ECG signal[D]. Tianjin:Tianjin University of Technology, 2016:1-3.)
[3] 李朝荣,朱利红,何苏,等.指纹特征和指静脉特征融合的身份认证[J].宜宾学院学报,2016,16(12):36-39. (LI C R, ZHU L H, HE S, et al. Identity authentication combing the features of fingerprint and finger-vein[J]. Journal of Yibin University, 2016, 16(12):36-39.)
[4] 桑海峰,武红娇,何大阔.手形、掌纹和掌静脉多特征融合识别[J].仪器仪表学报,2015,36(6):1356-1362. (SANG H F, WU H J, HE D K. Hand shape, palmprint and palm vein multimodal fusion recognition[J]. Chinese Journal of Scientific Instrument, 2015, 36(6):1356-1362.)
[5] 林森,张俊宇,郭金玉,等.局部方向模式在非接触掌纹识别中的应用[J].仪器仪表学报,2015,36(1):201-208. (LIN S, ZHANG J Y, GUO J Y, et al. Application of local directional pattern in non-contact palmprint recognition[J]. Chinese Journal of Scientific Instrument, 2015, 36(1):201-208.)
[6] 惠晓威,张俊宇,林森,等.改进局部方向模式在掌脉识别中的应用[J]. 激光与光电子学进展,2015,52(7):071001. (HUI X W, ZHANG J Y, LIN S, et al.Application of improved local directional pattern in palm vein recognition[J]. Laser & Optoelectronics Progress, 2015, 52(7):071001.)
[7] 张晨.基于复杂网络与多小波的掌纹识别方法[D].青岛:青岛理工大学,2016:1-5. (ZHANG C. Palmprint recognition approach based on complex network and muilti-wavelet features[D]. Qingdao:Qingdao University of Technology, 2016:1-5.)
[8] 龚淼.红外光与可见光图像超小波域融合方法研究[D]. 镇江:江苏科技大学,2016:30-34. (GONG M. Research on fusion method of infrared and visible images in super-wavelet domain[D]. Zhenjiang:Jiangsu University of Science and Technology, 2016:30-34.)
[9] 陈木生,蔡植善.基于NSCT的红外与可见光图像融合方法研究[J].激光与光电子学进展,2015,52(6):061002. (CHEN M S, CAI Z S. Study on fusion of visual and infrared images based on NSCT[J]. Laser & Optoelectronics Progress, 2015, 52(6):061002.)
[10] 龚淼,张尤赛,李垣江.区域能量和图像自相似的NSCT域图像融合[J].传感器与微系统,2017,36(5):74-76. (GONG M, ZHANG Y S, LI H J. Fusion of images based on regional energy and image self-similarity in NSCT domain[J].Transducer and Microsystem Technologies, 2017, 36(5):74-76.)
[11] 李艳玮, 郑伟勇.改进对比度和空间频率的图像融合算法[J].计算机工程与设计,2017,38(9):2457-2463. (LI Y W, ZHENG W Y. Image fusion algorithm with improved contrast and spatial frequency[J]. Computer Engineering and Design, 2017, 38(9):2457-2463.)
[12] 张蕾,金龙旭,韩双丽,等.采用非采样Contourlet变换与区域分类的红外和可见光图像融合[J].光学精密工程,2015,23(3):810-818. (ZHANG L, JIN L X, HAN S L, et al. Fusion of infrared and visual images based on non-sampled Contourlet transform and region classification[J]. Optics and Precision Engineering, 2015, 23(3):810-818.)
[13] 徐涛,吴倩,牛海清,等.一种改进的瓷套式电缆终端红外图像边缘提取算法[J].电瓷避雷器,2016(6):26-31. (XU T, WU Q, NIU H Q, et al. An improved algorithm based on infrared image edge extraction of cable terminal[J]. Insulators and Surge Arresters, 2016(6):26-31.)
[14] 徐天扬,惠晓威,林森,等.基于小波灰度曲面的近红外手指静脉识别方法[J].激光与光电子学进展,2016,53(4):041005. (XU T Y, HUI X W, LIN S, et al. A near infrared finger vein recognition approach based on wavelet grayscale surface matching[J]. Laser & Optoelectronics Progress, 2016, 53(4):041005.)
[15] 林森,徐天扬,王颖.基于Gabor小波和NBP算法的手掌静脉识别[J].激光与光电子学进展,2017,54(5):051002. (LIN S, XU T Y, WANG Y. Palm vein recognition based on Gabor wavelet and NBP algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(5):051002.)
[16] LIN S, WANG Y, XU T, et al. Palmprint and palm vein multimodal fusion biometrics based on MMNBP[C]//CCBR 2016:Proceedings of the 2016 Chinese Conference on Biometric Recognition, LNCS 9967. Cham:Springer, 2016:326-336.
[17] 吴微,苑玮琦,林森,等.手掌静脉识别典型波长选择[J].光学学报,2012,32(12):133-139. (WU W, YUAN W Q, LIN S, et al. Selection of typical wavelength for palm vein recognition[J]. Acta Optica Sinica, 2012, 32(12):133-139.)
[18] 陈朋,孙中海,党源杰,等.一种基于小波变换和Gabor滤波器的掌纹掌静脉图像层融合方法:中国,CN106022218A[P]. 2016-10-12. (CHEN P, SUN Z H, DANG Y J, et al. A palmprint palm vein image layer fusion method based on wavelet transform and Gabor filter:China, CN106022218A[P]. 2016-10-12.)
[19] 袁玲,黄靖,刘娅琴,等.基于图像融合的去掌纹手掌静脉图像增强方法[J]. 科学技术与工程,2016,16(36):48-54. (YUAN L, HUANG J, LIU Y Q, et al. Developed palm vein image enhancement based on image fusion[J]. Science Technology and Engineering, 2016, 16(36):48-54.)
[20] 张秀峰,张真林,谢红.掌纹ROI分割算法的研究与实现[J].计算机科学,2016,43(S2):170-173. (ZHANG X F, ZHANG Z L, XIE H. Research and realization of palmprint ROI segmentation algorithm[J]. Computer Science, 2016, 43(S2):170-173.)

Palmprint and palmvein image fusion recognition algorithm based on super-wavelet domain

掌纹掌脉图像超小波域融合识别算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	TAN Zhaoyi, CHEN Baifan. Enhanced vehicle 3D surround view based on coordinate inverse mapping [J]. Journal of Computer Applications, 2021, 41(4): 1165-1171.
[2]	Taiheng LIU, Zhaoshui HE. Surface defect detection method based on auto-encoding and knowledge distillation [J]. Journal of Computer Applications, 2021, 41(11): 3200-3205.
[3]	ZHANG Yonghong, LIU Hao, TIAN Wei, WANG Jiangeng. Rainfall cloud segmentation method in Tibet based on DeepLab v3 [J]. Journal of Computer Applications, 2020, 40(9): 2781-2788.
[4]	ZHANG Shuping, WU Wen, WAN Yi. Single image shadow removal method based on multistage generative adversarial network [J]. Journal of Computer Applications, 2020, 40(8): 2378-2385.
[5]	YUAN Yuan, WU Wen, WAN Yi. Single image shadow detection method based on entropy driven domain adaptive learning [J]. Journal of Computer Applications, 2020, 40(7): 2131-2136.
[6]	ZHOU Siyu, BAO Guoqi, LIU Kai. lmage vignetting correction based on constrained log-intensity entropy under low-pass filtering [J]. Journal of Computer Applications, 2020, 40(6): 1812-1817.
[7]	FU Lei, REN Dejun, WU Huayun, GAO Ming, QIU Lyu, HU Yunqi. Real-time segmentation algorithm for bubble defects of plastic bottle based on improved Fast-SCNN [J]. Journal of Computer Applications, 2020, 40(6): 1824-1829.
[8]	XU Jinrong, GUO Caiping, TONG Endong. Time-aware QoS prediction for SOA-based remote sensing image processing platform [J]. Journal of Computer Applications, 2020, 40(6): 1714-1721.
[9]	LIANG Xuehui, CHENG Yunze, ZHANG Ruijie, ZHAO Fei. Bridge crack classification and measurement method based on deep convolutional neural network [J]. Journal of Computer Applications, 2020, 40(4): 1056-1061.
[10]	MA Minyao, XU Yi, LIU Zhuo. Privacy preserving Hamming distance computing problem of DNA sequences [J]. Journal of Computer Applications, 2019, 39(9): 2636-2640.
[11]	LIAO Bin, TAN Daoqiang, WU Wen. Single image shadow removal based on attenuated generative adversarial networks [J]. Journal of Computer Applications, 2019, 39(9): 2712-2718.
[12]	CHEN Longbiao, CHEN Yuzhang, WANG Xiaochen, ZOU Peng, HU Xuemin. Underwater image super-resolution reconstruction method based on deep learning [J]. Journal of Computer Applications, 2019, 39(9): 2738-2743.
[13]	XUE Qian, LUO Qijun, WANG Yue. Crack detection for aircraft skin based on image analysis [J]. Journal of Computer Applications, 2019, 39(7): 2116-2120.
[14]	WANG Lishun, ZHONG Yong, LI Zhendong, HE Yilong. On-line fabric defect recognition algorithm based on deep learning [J]. Journal of Computer Applications, 2019, 39(7): 2125-2128.
[15]	HAN Jianghong, YUAN Jiaxuan, WEI Xing, LU Yang. Pedestrian visual positioning algorithm for underground roadway based on deep learning [J]. Journal of Computer Applications, 2019, 39(3): 688-694.