首页 > 论文 > 激光与光电子学进展 > 56卷 > 10期(pp:101010--1)

结合深度神经网络和随机森林的手掌静脉分类

Palm Vein Classification Based on Deep Neural Network and Random Forest

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

提出了一种结合深度神经网络和随机森林的手掌静脉分类新方法。利用预训练深度神经网络模型AlexNet提取掌脉特征,采用主成分分析法对提取的高维掌静脉特征进行降维处理,以减少存储空间、降低分类误差,结合对噪声具有很好容忍能力的随机森林进行分类。基于香港理工(PolyU)数据库、中国科学院(CASIA)数据库和自建库的测试精度分别为100%、97.00%和99.50%。相较传统方法,所提方法避免了人工选择特征提取算法的局限性,有效降低了手掌静脉的分类误差, 具有更好的稳健性。

Abstract

A new palm vein classification method that combines a deep neural network and a random forest is proposed. First, the proposed method extracts the palm vein features using AlexNet, a pre-trained deep neural network model. Then, the principal component analysis is used to reduce the dimensionality of the extracted high-dimensional palm vein features in order to conserve storage space and reduce classification errors. Finally, the random forest is used for classification owing to its high tolerance to noise. Based on the PolyU, CASIA, and self-built databases, the test accuracies obtained are 100%, 97.00%, and 99.50%, respectively. Compared with the traditional methods, the proposed method overcomes the limitations of the manual feature extraction algorithms, effectively reduces the palm vein classification errors, and demonstrates better robustness.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391

DOI:10.3788/lop56.101010

所属栏目:图像处理

基金项目:国家自然科学基金(61771233)、广东省重点科技项目(2013A022100009)

收稿日期:2018-10-30

修改稿日期:2018-12-06

网络出版日期:2018-12-12

作者单位    点击查看

袁丽莎:南方医科大学生物医学工程学院, 广东 广州 510515
娄梦莹:南方医科大学生物医学工程学院, 广东 广州 510515
刘娅琴:南方医科大学生物医学工程学院, 广东 广州 510515
杨丰:南方医科大学生物医学工程学院, 广东 广州 510515
黄靖:南方医科大学生物医学工程学院, 广东 广州 510515

联系人作者:黄靖(jhuangyg@smu.edu.cn); 刘娅琴(liuyq@smu.edu.cn);

【1】Jia W, Zhang B, Lu J T, et al. Palmprint recognition based on complete direction representation[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4483-4498.

【2】Hrechak A K, McHugh J A. Automated fingerprint recognition using structural matching[J]. Pattern Recognition, 1990, 23(8): 893-904.

【3】Lee J C. A novel biometric system based on palm vein image[J]. Pattern Recognition Letters, 2012, 33(12): 1520-1528.

【4】Wei Z S, Qiu X C, Sun Z N, et al. Counterfeit iris detection based on texture analysis[C]∥2008 19th International Conference on Pattern Recognition, December 8-11, 2008, Tampa, FL, USA. New York: IEEE, 2008: 1-4.

【5】Zhou B, He Y Q, Wang J. Face recognition based on adaptive neighborhood locality preserving projection algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(3): 031010.
周博, 何宇清, 王建. 基于自适应近邻局部保持投影算法的人脸识别[J]. 激光与光电子学进展, 2018, 55(3): 031010.

【6】Li Q. The theoretical and experimental study on palm vein recognition technology[D]. Wuhan: Huazhong University of Science and Technology, 2010.
李强. 掌静脉身份识别技术的理论与实验研究[D]. 武汉: 华中科技大学, 2010.

【7】Watanabe M. Palm vein authentication[M]. London: Springer, 2008: 75-88.

【8】Zhou Y B, Kumar A. Human identification using palm-vein images[J]. IEEE Transactions on Information Forensics and Security, 2011, 6(4): 1259-1274.

【9】Xu X Y, Yao P. Palm vein recognition algorithm based on HOG and improved SVM[J]. Computer Engineering and Applications, 2016, 52(11): 175-180, 214.
徐笑宇, 姚鹏. 基于HOG与改进的SVM的手掌静脉识别算法[J]. 计算机工程与应用, 2016, 52(11): 175-180, 214.

【10】Lee Y P. Palm vein recognition based on a modified (2D)2LDA[J]. Signal, Image and Video Processing, 2015, 9(1): 229-242.

【11】Zhou Y J, Liu Y Q, Yang F, et al. Palm-vein recognition based on oriented features[J]. Journal of Image and Graphics, 2014, 19(2): 243-252.
周宇佳, 刘娅琴, 杨丰, 等. 基于方向特征的手掌静脉识别[J]. 中国图象图形学报, 2014, 19(2): 243-252.

【12】Wang R, Wang G Y, Chen Z, et al. A palm vein identification system based on Gabor wavelet features[J]. Neural Computing and Applications, 2014, 24(1): 161-168.

【13】Mirmohamadsadeghi L, Drygajlo A. Palm vein recognition with local texture patterns[J]. IET Biometrics, 2014, 3(4): 198-206.

【14】Li J L, Wang H B, Tao L. Palm vein and palmprint fusion recognition with those two features existing in same near-infrared palm image[J]. Computer Engineering and Applications, 2018, 54(9): 156-164, 236.
李俊林, 王华彬, 陶亮. 单幅近红外手掌图像掌静脉和掌纹多特征识别[J]. 计算机工程与应用, 2018, 54(9): 156-164, 236.

【15】Li X C, Zhang C H, Lin S. Palmprint and palm vein feature fusion recognition based on BSLDP and canonical correlation analysis[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051012.
李新春, 张春华, 林森. 基于BSLDP和典型相关分析的掌纹掌脉融合识别[J]. 激光与光电子学进展, 2018, 55(5): 051012.

【16】LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

【17】Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

【18】He K M, Zhang X Y, Ren S Q, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1026-1034.

【19】Li J N, Zhang B H. Face recognition by feature matching fusion combined with improved convolutional neural network[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101504.
李佳妮, 张宝华. 特征匹配融合结合改进卷积神经网络的人脸识别[J]. 激光与光电子学进展, 2018, 55(10): 101504.

【20】Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

【21】Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1520-1528.

【22】Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.

【23】Shao L, Zhu F, Li X L. Transfer learning for visual categorization: a survey[J].IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(5): 1019-1034.

【24】Ghazi M M, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters[J]. Neurocomputing, 2017, 235: 228-235.

【25】Sargano A B, Wang X F, Angelov P, et al. Human action recognition using transfer learning with deep representations[C]∥2017 International Joint Conference on Neural Networks (IJCNN), May 14-19, 2017, Anchorage, AK, USA. New York: IEEE, 2017: 463-469.

【26】Kumar A, Wang K. Identifying humans by matching their left palmprint with right palmprint images using convolutional neural network[C]∥The First International Workshop on Deep Learning and Pattern Recognition(DLPR 2016), December 4-8, 2016, Cancun, Mexico. [S.l.:s.n.]. 2016: 1-6.

【27】Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks?[C]∥Advances in Neural Information Processing Systems 27 (NIPS 2014), December 8-13, 2014, Palais des Congrès de Montréal, Montréal, Canada. [S.l.:s.n.]. 2014: 3320-3328.

【28】Chen P. Principal component analysis and its application in feature extraction[D]. Xi''an: Shaanxi Normal University, 2014.
陈佩. 主成分分析法研究及其在特征提取中的应用[D]. 西安: 陕西师范大学, 2014.

【29】Breiman L.Radom forests[J]. Machine Learning, 2001, 45(1): 5-32.

【30】Li H. Statistical learning method[M]. Beijing: Tsinghua University Press, 2012.
李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012.

【31】Liu Y Q, Zhou Y J, Qiu S R, et al. Real-time locating method for palmvein image acquisition[M]. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 94-110.

引用该论文

Yuan Lisha,Lou Mengying,Liu Yaqin,Yang Feng,Huang Jing. Palm Vein Classification Based on Deep Neural Network and Random Forest[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101010

袁丽莎,娄梦莹,刘娅琴,杨丰,黄靖. 结合深度神经网络和随机森林的手掌静脉分类[J]. 激光与光电子学进展, 2019, 56(10): 101010

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF