掌纹识别研究进展综述

doi:10.16451/j.cnki.issn1003-6059.201905006

Abstract
Figure/Table
References
Related Citation (2)

Download: PDF (835 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract Palmprint images contain rich features and they can be easily combined with hand dorsal veins, finger-knuckle-prints and hand shapes to form multimodal features. Thus, palmprint recognition becomes a hot topic in the field of biometric recognition. In this paper, the basic process of palmprint recognition is discussed from three aspects: the collection of palmprint images, region of interest detection, and feature extraction and matching. Multimodal methods based on fusion of different features are also explored. Besides, on the basis of feature extraction means, palmprint recognition algorithms are roughly divided into hand-craft based algorithms including encoding, structure and statistics, and subspace and feature learning based algorithms including machine learning and deep learning. The algorithms are compared and analyzed in detail. Finally, challenges and future perspectives in palmprint recognition are discussed, especially palmprint recognition system in complex environment across different devices.

Key words： Palmprint Recognition Region of Interest(ROI) Extraction Feature Representation and Matching Multimodal Fusion

Received: 25 December 2018

ZTFLH:

TP 181

Fund:Supported by National Natural Science Foundation of China(No.61105021), Natural Science Foundation of Zhejiang Province (No.LGF19F030002)

Corresponding Authors: (ZHONG Dexing(Corresponding author), Ph.D., associate professor. His research interests include computer vision and biometric recognition.)

About author:: (ZHU Jinsong, Ph.D.candidate. His research interests include computer vision and biometric recognition.)(DU Xuefeng, undergraduate.)

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHONG Dexing¹
	ZHU Jinsong¹
	DU Xuefeng¹

Cite this article:

ZHONG Dexing¹,ZHU Jinsong¹,DU Xuefeng¹. Progress of Palmprint Recognition: A Review[J]. , 2019, 32(5): 436-445.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201905006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I5/436

[1] ZHANG D, ZUO W M, YUE F. A Comparative Study of Palmprint Recognition Algorithms. ACM Computing Surveys, 2012, 44(1). DOI: 10.1145/2071389.2071391.
[2] ZHONG D X, DU X F, ZHONG K C. Decade Progress of Palmprint Recognition: A Brief Survey. Neurocomputing, 2019, 328: 16-28.
[3] FEI K, XU Y, ZHANG B, et al. Low-Rank Representation Integrated with Principal Line Distance for Contactless Palmprint Recognition. Neurocomputing, 2016, 218: 264-275.
[4] LENG L, LI M, KIM C, et al. Dual-Source Discrimination Power Analysis for Multi-instance Contactless Palmprint Recognition. Multi-media Tools and Applications, 2017, 76(1): 333-354.
[5] ZHANG L, LI L D, YANG A Q, et al. Towards Contactless Palmprint Recognition: A Novel Device, a New Benchmark, and a Co-llaborative Representation Based Identification Approach. Pattern Recognition, 2017, 69: 199-212.
[6] SUN Z N, TAN T N, WANG X B, et al. Ordinal Palmprint Represention for Personal Identification // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 279-284.
[7] GUO Z H, ZHANG D, ZHANG L, et al. Empirical Study of Light Source Selection for Palmprint Recognition. Pattern Recognition Le-tters, 2011, 32(2): 120-126.
[8] YANG B, XIANG X Q, XU D Q, et al. 3D Palmprint Recognition Using Shape Index Representation and Fragile Bits. Multimedia Tools and Applications, 2017, 76(14): 15357-15375.
[9] ZHANG D , LU G M, LI W, et al. Palmprint Recognition Using 3D Information. IEEE Transactions on Systems, Man, and Cybernetics(Applications and Reviews), 2009, 39(5): 505-519.
[10] ZHANG D D, KONG W K A, YOU J, et al. Online Palmprint Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9): 1041-1050.
[11] KUMAR A, SHEKHAR S. Personal Identification Using Multibiometrics Rank-Level Fusion. IEEE Transactions on Systems, Man, and Cybernetics(Applications and Reviews), 2011, 41(5): 743-752.
[12] FERRER M A, VARGAS F, MORALES A. Bispectral Contactless Hand Based Biometric System // Proc of the 2nd National Conference on Telecommunications. Washington, USA: IEEE, 2011: 1-6.
[13] CHORAS/ M, KOZIK R. Contactless Palmprint and Knuckle Biometrics for Mobile Devices. Pattern Analysis and Applications, 2012, 15(1): 73-85.
[14] JIA W, HU R X, GUI J, et al. Palmprint Recognition across Di-fferent Devices. Sensors, 2012, 12(6): 7938-7964.
[15] UNGUREANU A S, THAVALENGAL S, COGNARD T E, et al. Unconstrained Palmprint as a Smartphone Biometric. IEEE Transactions on Consumer Electronics, 2017, 63(3): 334-342.
[16] HONG D F, SU J, HONG Q G, et al. Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese-Osher Decomposition Model. PLoS One, 2014, 9(7). DOI: 10.1371/journal.pone.0101866.
[17] HAMMAMI M, BEN JEMAA S, BEN-ABDALLAH H. Selection of Discriminative Sub-regions for Palmprint Recognition. Multimedia Tools and Applications, 2014, 68(3): 1023-1050.
[18] BADRINATH G S, GUPTA P. Palmprint Based Recognition System Using Phase-Difference Information. Future Generation Computer Systems, 2012, 28(1): 287-305.
[19] TIWARI K, ARYA D K, BADRINATH G S, et al. Designing Palmprint Based Recognition System Using Local Structure Tensor and Force Field Transformation for Human Identification. Neurocomputing, 2013, 116: 222-230.
[20] FEI L K, XU Y, TANG W L, et al. Double-Orientation Code and Nonlinear Matching Scheme for Palmprint Recognition. Pattern Recognition, 2016, 49: 89-101.
[21] PAN X, RUAN Q Q. Palmprint Recognition Using Gabor-Based Local Invariant Features. Neurocomputing, 2009, 72(7/8/9): 2040-2045.
[22] MU M R, RUAN Q Q. Region Covariance Matrices as Feature Descriptors for Palmprint Recognition Using Gabor Features. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25(4): 513-528.
[23] TAMRAKAR D, KHANNA P. Blur and Occlusion Invariant Palmprint Recognition with Block-Wise Local Phase Quantization Histogram. Journal of Electronic Imaging, 2015, 24(4): 043006.
[24] WU Q E, CHEN Z W, HAN R J, et al. A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(2): 1656002.
[25] YUE F, ZUO W M, ZHANG D, et al. Orientation Selection Using Modified FCM for Competitive Code-Based Palmprint Recognition. Pattern Recognition, 2009, 42(11): 2841-2849.
[26] SHEN L L, WU S P, ZHENG S H, et al. Embedded Palmprint Recognition System Using OMAP 3530. Sensors, 2012, 12(2): 1482-1493.
[27] LI C, LIU F, ZHANG Y Z. A Principal Palm-Line Extraction Method for Palmprint Images Based on Diversity and Contrast // Proc of the 3rd International Congress on Image and Signal Proce-ssing. Berlin, Germany: Springer, 2010: 1772-1777.
[28] PARIHAR A S, KUMAR A, VERMA O P, et al. Point Based Features for Contact-Less Palmprint Images // Proc of the IEEE International Conference on Technologies for Homeland Security. Washington, USA: IEEE, 2013: 165-170.
[29] BRUNO A, CARMINETTI P, GENTILE V, et al. Palmprint Principal Lines Extraction // Proc of the IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications. Washington, USA: IEEE, 2014: 50-56.
[30] ALI M M H, YANAWAR P, GAIKWAD A T. Study of Edge Detection Methods Based on Palmprint Lines // Proc of the International Conference on Electrical, Electronics, and Optimization Techniques. Berlin, Germany: Springer, 2016: 1344-1350.
[31] 许学斌,张德运,张新曼,等.基于离散曲波变换和支持向量机的掌纹识别方法.红外与毫米波学报, 2009, 28(6): 456-460.
(XU X B, ZHANG D Y, ZHANG X M, et al. Palmprint Recognition Based on Discrete Curvelet Transform and Support Vector Machine. Journal of Infrared and Millimeter Waves, 2009, 28(6): 456-460.)
[32] ZHANG L, LI H Y. Encoding Local Image Patterns Using Riesz Transforms: With Applications to Palmprint and Finger-Knuckle-Print Recognition. Image and Vision Computing, 2012, 30(12): 1043-1051.
[33] XU X B, LU L B, ZHANG X M, et al. Multispectral Palmprint Recognition Using Multiclass Projection Extreme Learning Machine and Digital Shearlet Transform. Neural Computing and Applications, 2016, 27(1): 143-153.
[34] GAYATHRI R, RAMAMOORTHY P. Automatic Palmprint Identification Based on High Order Zernike Moment. American Journal of Applied Sciences, 2012, 9(5): 759-765.
[35] GAO X J, LUO X L, PAN X, et al. Palmprint Recognition Based on Deep Learning // Proc of the International Conference on Wireless Mobile and Multi-media. Berlin, Germany: Springer, 2015. DOI: 10.1049/cp.2015.0942.
[36] LIU D, SUN D M. Contactless Palmprint Recognition Based on Convolutional Neural Network // Proc of the IEEE International Conference on Signal Processing. Washington, USA: IEEE, 2016: 1363-1367.
[37] SUN Q L, ZHANG J X, YANG A Q, et al. Palmprint Recognition with Deep Convolutional Features // Proc of the Chinese Confe-rence on Image and Graphics Technologies. Berlin, Germany: Springer, 2017: 12-19.
[38] SVOBODA J, MASCI J, BRONSTEIN M M. Palmprint Recognition via Discriminative Index Learning // Proc of the 23rd International Conference on Pattern Recognition. Washington, USA: IEEE, 2016: 4232-4237.
[39] LIN Z, CHENG Z X, YING S, et al. Palmprint and Palmvein Recognition Based on DCNN and a New Large-Scale Contactless Palmvein Dataset. Symmetry, 2018, 10(4). DOI: 10.3390/sym10040078.
[40] BAO X J, GUO Z H. Extracting Region of Interest for Palmprint by Convolutional Neural Networks // Proc of the 6th International Conference on Image Processing Theory, Tools and Applications. Washington, USA: IEEE, 2016. DOI:10.1109/IPTA.2016.7820994.
[41] IZADPANAHKAKHK M, RAZAVI S, TAGHIPOUR-GORJIKOL-AIE M, et al. Deep Region of Interest and Feature Extraction Mo-dels for Palmprint Verification Using Convolutional Neural Networks Transfer Learning. Applied Sciences, 2018, 8(7). DOI: 10.3390/app8071210.
[42] WANG G X, KANG W X, WU Q X, et al. Generative Adversarial Network(GAN) Based Data Augmentation for Palmprint Recognition // Proc of the Conference on Digital Image Computing: Techniques and Applications. Washington, USA: IEEE, 2018. DOI: 10.1109/DICTA.2018.8615782.
[43] LIU Y, KUMAR A. A Deep Learning Based Framework to Detect and Recognize Humans Using Contactless Palmprints in the Wild[C/OL]. [2018-12-12]. https://arxiv.org/pdf/1812.11319.pdf.
[44] ZHONG D X, YANG Y, DU X F. Palmprint Recognition Using Siamese Network // Proc of the 13th Chinese Conference on Biome-tric Recognition. Berlin, Germany: Springer, 2018: 48-55.
[45] ZHONG D X, ZHU J S. Centralized Large Margin Cosine Loss for Open-set Deep Palmprint Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2019. DOI: 10.1109/TCSVT.2019.2904283.
[46] WANG Y X, RUAN Q Q. Dual-Tree Complex Wavelet Transform Based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition. Computing and Informatics, 2009, 28(3): 299-318.
[47] MERAOUMIA A, CHITROUB S, BOURIDANE A. Palmprint and Finger-Knuckle-Print for Efficient Person Recognition Based on Log-Gabor Filter Response. Analog Integrated Circuits and Signal Processing, 2011, 69(1): 17-27.
[48] ZHANG L, LI H Y, NIU J Y. Fragile Bits in Palmprint Recognition. IEEE Signal Processing Letters, 2012, 19(10): 663-666.
[49] CHEN H P. An Efficient Palmprint Recognition Method Based on Block Dominat Orientation Code. Optik, 2015, 126(21): 2869-2875.
[50] HONG D F, LIU W Q, SU J, et al. A Novel Hierarchical Approach for Multispectral Palmprint Recognition. Neurocomputing, 2015, 151: 511-521.
[51] TAMRAKAR D, KHANNA P. Kernel Discriminant Analysis of Block-Wise Gaussian Derivative Phase Pattern Histogram for Palmprint Recognition. Journal of Visual Communication and Image Representation, 2016, 40: 432-448.
[52] FEI L K, ZHANG B, XU Y, et al. Palmprint Recognition Using Neighboring Direction Indicator. IEEE Transactions on Human-Machine Systems, 2016, 46(6): 787-798.
[53] PAN X, RUAN Q Q. Palmprint Recognition with Improved Two-Dimensional Locality Preserving Projections. Image and Vision Computing, 2008, 26(9): 1261-1268.
[54] ZUO W M, ZHANG H Z, ZHANG D, et al. Post-Processed LDA for Face and Palmprint Recognition: What Is the Rationale. Signal Processing, 2010, 90(8): 2344-2352.
[55] MU M R, RUAN Q Q. Mean and Standard Deviation as Features for Palmprint Recognition Based on Gabor Filters. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25(4): 491-512.
[56] ZUO W M, YUE F, ZHANG D. On Accurate Orientation Extraction and Appropriate Distance Measure for Low-Resolution Palmprint Recognition. Pattern Recognition, 2011, 44(4): 964-972.
[57] XU X P, GUO Z H, SONG C J, et al. Multispectral Palmprint Recognition Using a Quaternion Matrix. Sensors, 2012, 12(4): 4633-4647.
[58] MERAOUMIA A, CHITROUB S, BOURIDANE A. 2D and 3D Palmprint Information, PCA and HMM for an Improved Person Recognition Performance. Integrated Computer-Aided Enginee-ring, 2013, 20(3): 303-319.
[59] ALTUN A A. A Combination of Genetic Algorithm, Particle Swarm Optimization and Neural Network for Palmprint Recognition. Neural Computing and Applications, 2013, 22(S1): 27-33.
[60] RAGHAVENDRA R, BUSCH C. Novel Image Fusion Scheme Ba-sed on Dependency Measure for Robust Multispectral Palmprint Recognition. Pattern Recognition, 2014, 47(6): 2205-2221.
[61] SUN Z N, WANG L B, TAN T N. Ordinal Feature Selection for Iris and Palmprint Recognition. IEEE Transactions on Image Processing, 2014, 23(9): 3922-3934.
[62] JIA W, HU R X, LEI Y K, et al. Histogram of Oriented Lines for Palmprint Recognition. IEEE Transactions on Systems, Man, and Cybernetics(Systems), 2014, 44(3): 385-395.
[63] LAADJEL M, AL-MAADEED S, BOURIDANE A. Combining Fi-sher Locality Preserving Projections and Passband DCT for Efficient Palmprint Recognition. Neurocomputing, 2015, 152: 179-189.
[64] HONG D F, LIU W Q, WU X, et al. Robust Palmprint Recognition Based on the Fast Variation Vese-Osher Model. Neurocompu-ting, 2016, 174: 999-1012.
[65] BINGOL O, EKINCI M. Stereo-Based Palmprint Recognition in Va-rious 3D Postures. Expert Systems with Applications, 2017, 78: 74-88.
[66] CHAA M, BOUKEZZOULA N E, ATTIA A. Score-Level Fusion of Two-Dimensional and Three-Dimensional Palmprint for Personal Recognition Systems. Journal of Electronic Imaging, 2017, 26(1).DOI:10.1117/1.JEI.26.1.013018.
[67] LU J W, ZHAO Y W, XUE Y X, et al. Palmprint Recognition via Locality Preserving Projections and Extreme Learning Machine Neural Network // Proc of the 9th International Conference on Signal Processing. Washington, USA: IEEE, 2008: 2097-2100.
[68] HUANG L L, LI N. Palmprint Recognition Using Polynomial Neural Network // Proc of the 7th International Conference on Advances in Neural Networks. Berlin, Germany: Springer, 2010, II: 208-213.
[69] GAO X J, LUO X L, PAN X, et al. Palmprint Recognition Based on Deep Learning // Proc of the 6th International Conference on Wireless, Mobile and Multi-Media. Berlin, Germany: Springer, 2015: 214-216.
[70] GUO Z H, ZHANG D, ZHANG L, et al. Feature Band Selection for Online Multispectral Palmprint Recognition. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 1094-1099.
[71] JIA W, ZHANG B, LU J T, et al. Palmprint Recognition Based on Complete Direction Representation. IEEE Transactions on Image Processing, 2017, 26(9): 4483-4498.
[72] ZHONG D X, LI M H, SHAO H K, et al. Palmprint and Dorsal Hand Vein Dualmodal Biometrics // Proc of the IEEE International Conference on Multimedia and Expo Workshops. Washington, USA: IEEE, 2018. DOI: 10.1109/ICMEW.2018.8551582.
[73] WANG H G, YAU W Y, SUWANDY A, et al. Person Recognition by Fusing Palmprint and Palm Vein Images Based on "Laplacianpalm" Representation. Pattern Recognition, 2008, 41(5): 1514-1527.
[74] XU J, CUI J J, XUE D Y, et al. Near Infrared Vein Image Acquisition System Based on Image Quality Assessment // Proc of the International Conference on Electronics, Communications and Control. Berlin, Germany: Springer, 2011: 922-925.
[75] DAI J F, ZHOU J. Multifeature-Based High-Resolution Palmprint Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 945-957.
[76] LU J W, TAN Y P. Improved Discriminant Locality Preserving Projections for Face and Palmprint Recognition. Neurocomputing, 2011, 74(18): 3760-3767.
[77] XU Y, FAN Z Z, QIU M N, et al. A Sparse Representation Me-thod of Bimodal Biometrics and Palmprint Recognition Experiments. Neurocomputing, 2013, 103: 164-171.
[78] YANG B, WANG X H, YAO J L, et al. Efficient Local Representations for Three-Dimensional Palmprint Recognition. Journal of Electronic Imaging, 2013, 22(4). DOI: 10.1117/1.JEI.22.4.043040.
[79] ZHANG S W, GU X X. Palmprint Recognition Method Based on Score Level Fusion. Optik, 2013, 124(18): 3340-3344.
[80] CUI J R. Multispectral Fusion for Palmprint Recognition. Optik, 2013, 124(17): 3067-3071.
[81] CUI J R. 2D and 3D Palmprint Fusion and Recognition Using PCA Plus TPTSR Method. Neural Computing and Applications, 2014, 24(3/4): 497-502.
[82] NI J Y, LUO J, LIU W B. 3D Palmprint Recognition Using Dem-pster-Shafer Fusion Theory. Journal of Sensors, 2015.DOI:10.1155/2015/252086.
[83] PERUMAL E, RAMACHANDRAN S. A Multimodal Biometric Sy-stem Based on Palmprint and Finger Knuckle Print Recognition Methods. International Arab Journal of Information Technology, 2015, 12(2): 118-128.
[84] BOUCHEMHA A, DOGHMANE N, NAIT-HAMOUD M C, et al. Multispectral Palmprint Recognition Methodology Based on Multiscale Representation. Journal of Electronic Imaging, 2015, 24(4).DOI:10.1117/1.JEI.24.4.043005.
[85] BOUNNECHE M D, BOUBCHIR L, BOURIDANE A, et al. Mu-lti-spectral Palmprint Recognition Based on Oriented Multiscale Log-Gabor Filters. Neurocomputing, 2016, 205: 274-286.
[86] LU L B, ZHANG X M, XU X B, et al. Multispectral Image Fusion for Illumination-Invariant Palmprint Recognition. PLOS ONE, 2017, 12(5). DOI: 10.1371/journal.pone.0178432.
[87] TABEJAMAAT M, MOUSAVI A. A Coding-Guided Holistic-Based Palmprint Recognition Approach. Multimedia Tools and Applications, 2017, 76(6): 7731-7747.
[88] LUO Y T, ZHAO L Y, ZHANG B, et al. Local Line Directional Pattern for Palmprint Recognition. Pattern Recognition, 2016, 50: 26-44.
[89] CHEN J L, GUO Z H. Palmprint Matching by Minutiae and Ridge Distance // Proc of the International Conference on Cloud Computing and Security. Berlin, Germany: Springer, 2016: 371-382.
[90] SAEDI S, CHARKARI N M. Palmprint Authentication Based on Discrete Orthonormal S-Transform. Applied Soft Computing, 2014, 21: 341-351.
[91] BADRINATH G S, KACHHI N K, GUPTA P. Verification System Robust to Occlusion Using Low-Order Zernike Moments of Palmprint Sub-images. Telecommunication Systems, 2011, 47(3/4): 275-290.
[92] BAI X F, GAO N, ZHANG Z H, et al. 3D Palmprint Identification Combining Blocked ST and PCA. Pattern Recognition Letters, 2017, 100: 89-95.

[93] GUI J, JIA W, ZHU L, et al. Locality Preserving Discriminant Projections for Face and Palmprint Recognition. Neurocomputing, 2010, 73(13/14/15): 2696-2707.
[94] FARMANBAR M, TOYGAR O. Feature Selection for the Fusion of Face and Palmprint Biometrics. Signal Image and Video Proce-ssing, 2016, 10(5): 951-958.
[95] CHENG J D, SUN Q L, ZHANG J X, et al. Supervised Hashing with Deep Convolutional Features for Palmprint Recognition // Proc of the Chinese Conference on Biometric Recognition. Berlin, Germany: Springer, 2017: 259-268.
[96] GEORGE D, LEHRACH W, KANSKY K, et al. A Generative Vision Model That Trains with High Data Efficiency and Breaks Text-Based CAPTCHAs. Science, 2017, 358(6368). DOI: 10.1126/science.aag2612.