掌纹识别研究进展综述
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摘要
掌纹图像蕴含丰富特征,容易与手背静脉、指节纹及手形特征进行多模态融合,因此成为生物特征识别领域的热点.文中主要从掌纹的采集、感兴趣区域的检测、特征提取与匹配3方面介绍掌纹识别的基本流程.探讨基于不同特征融合的多模态识别策略.根据特征提取方法的不同,掌纹识别算法可分为基于手工设计的算法(如编码特征、结构特征、统计特征、子空间特征)和基于特征学习的算法(如机器学习和深度学习),文中对上述算法进行详细对比和分析.最后讨论未来掌纹识别面临的挑战和发展,特别是复杂场景下跨平台的掌纹识别系统.
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.
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.
引文
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[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.Multimedia 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 Collaborative 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 Letters,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 3DInformation.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 Different 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.PLo S 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 Processing.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.)
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