|
|
|
| Finger-Knuckle-Print Recognition: A Preliminary Review |
| LU Jingting1,2, JIA Wei2, YE Hui1, ZHAO Yang2, MIN Hai2, YU Ye2, HU Rongxiang3 |
1.Institute of Industry and Equipment Technology, Hefei University of Technology, Hefei 230009
2.School of Computer and Information, Hefei University of Technology, Hefei 23009
3.Institute of Nuclear Energy Safety Technology, Hefei Institutes of Physical Science, Chinese Academy of Sceinces, Hefei 230031 |
|
|
|
|
Abstract Compared with face, fingerprint, and iris based biometrics systems, finger-knuckle-print recognition based biometrics system has stable features, and it can be collected by low cost device and be easily combined with palmprint, finger vein, and hand shape recognition to form a robust system. In this paper, the definition, the data acquisition and the preprocessing of finger-knuckle-print recognition are firstly introduced. Then, the feature extraction and matching algorithms as well as multi-modal methods are reviewed. The effective finger-knuckle-print recognition algorithms are roughly divided into six categories: texture-based algorithm, structure-based algorithm, subspace learning-based algorithm, correlation filter-based algorithm, local descriptor-based algorithm and orientation coding-based algorithm. Finally, the development tendency of finger-knuckle-print recognition is forecasted.
|
|
Received: 25 August 2016
|
|
|
| Fund:Supported by National Natural Science Foundation of China(No.61673157,61402018,61305006,61305093,61370167,61175022) |
|
Corresponding Authors:
(JIA Wei(Corresponding author), born in 1978, Ph.D., associate professor. His research interests include pattern recognition and biometrics.)
|
About author:: (LU Jingting, born in 1983, Ph.D., lecturer. Her research interests include pattern recognition and biometrics.)
(JIA Wei(Corresponding author), born in 1978, Ph.D., associate professor. His research interests include pattern recognition and biometrics.)
(YE Hui, born in 1993, master student. Her research interests include pattern recognition and biometrics.)
(ZHAO Yang, born in 1987, Ph.D., associate professor. His research interests include pattern recognition and biometrics.)
(MIN Hai, born in 1985, Ph.D., lecturer. His research interests include pattern recognition and biometrics.)
(YU Ye, born in 1982, Ph.D., associate professor. Her research interests include pattern recognition and biometrics.)
(HU Rongxiang, born in 1983, Ph.D., assistant professor. His research interests include pattern recognition and biome-trics.) |
|
|
|
[1] ZHANG L, ZHANG L, ZHANG D, et al. Online Finger-Knuckle-Print Verification for Personal Authentication. Pattern Recognition, 2010, 43(7): 2560-2571.
[2] KUMAR A, XU Z H. Personal Identification Using Minor Knuckle Patterns from Palm Dorsal Surface. IEEE Transactions on Information Forensics and Security, 2016, 11(10): 2338-2348.
[3] KUMAR A. Importance of Being Unique from Finger Dorsal Patterns: Exploring Minor Finger Knuckle Patterns in Verifying Human Identities. IEEE Transactions on Information Forensics and Security, 2014, 9(8): 1288-1298.
[4] KANHANGAD V, KUMAR A, ZHANG D. A Unified Framework for Contactless Hand Verification. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 1014-1027.
[5] KANHANGAD V, KUMAR A, ZHANG D. Contactless and Pose Invariant Biometric Identification Using Hand Surface. IEEE Transactions on Image Processing, 2011, 20(5): 1415-1424.
[6] DHARMADHIKARI A P, GANORKAN S R. Human Identification Using Finger Images. International Journal of Research in Engineering and Technology, 2014, 3(5): 840-843.
[7] KUMAR A, RAVIKANTH C. Personal Authentication Using Finger Knuckle Surface. IEEE Transactions on Information Forensics and Security, 2009, 4(1): 98-110.
[8] YANG W M, HANG X L, ZHOU F, et al. Comparative Competitive Coding for Personal Identification by Using Finger Vein and Finger Dorsal Texture Fusion. Information Sciences, 2014, 268: 20-32.
[9] LI Q, QIU Z K, SUN D M, et al. Personal Identification Using Knuckleprint // Proc of the 5th Chinese Conference on Advances in Biometric Person Authentication. Berlin, Germany: Springer, 2004: 680-689.
[10] WOODARD D L, FLYNN P J. Personal Identification Utilizing Finger Surface Features // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005: 1030-1036.
[11] WOODARD D L, FLYNN P J. Finger Surface as a Biometric Identifier. Computer Vision and Image Understanding, 2005, 100(3): 357-384.
[12] RIBARIC S, FRATRIC I. A Biometric Identification System Based on Eigenpalm and Eigenfinger Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(1): 1698-1709.
[13] ZHANG L, ZHANG L, ZHANG D, et al. Ensemble of Local and Global Information for Finger-Knuckle-Print Recognition. Pattern Recognition, 2011, 44(9): 1990-1998.
[14] XU X M, JIN Q, ZHOU L, et al. Illumination-Invariant and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices. Sensors, 2015, 15(2): 4326-4352.
[15] KANG W X, CHEN X P, WU Q X. The Biometric Recognition on Contactless Multi-spectrum Finger Images. Infrared Physics and Technology, 2015, 68: 19-27.
[16] KUMAR A, WANG B C. Recovering and Matching Minutiae Patterns from Finger Knuckle Images. Pattern Recognition Letters, 2015, 68(P2): 361-367.
[17] KONG T, YANG G P, YANG L. A Hierarchical Classification Method for Finger Knuckle Print Recognition. EURASIP Journal on Advances in Signal Processing, 2014. DOI: 10.1186/1689-1680-2014-44.
[18] SHARIATMADAR Z S, FAEZ K. Finger-Knuckle-Print Recognition Performance Improvement via Multi-instance Fusion at the Score Level. Optik-International Journal for Light and Electron Optics, 2014, 125(3): 908-910.
[19] NIGAM A, TIWARI K, GUPTA P. Multiple Texture Information Fusion for Finger-Knuckle-Print Authentication System. Neurocomputing, 2016, 188: 190-205.
[20] RAVIKANTH C, KUMAR A. Biometric Authentication Using Finger-Back Surface // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2007. DOI: 10.1109/CVPR.2007.383390.
[21] JING X Y, LI W Q, LAN C, et al. Orthogonal Complex Locality Preserving Projections Based on Image Space Metric for Finger-Knuckle-Print Recognition // Proc of the International Conference on Hand-Based Biometrics. Washington, USA: IEEE, 2011. DOI: 10.1109/ICHB.2011.6094327.
[22] YANG W K, SUN C Y, ZHANG L. A Multi-manifold Discriminant Analysis Method for Image Feature Extraction. Pattern Recognition, 2011, 44(8): 1649-1657.
[23] CHEN Y, LI Z Z, JIN Z. Feature Extraction Based on Maximum Nearest Subspace Margin Criterion. Neural Processing Letters, 2013, 37(3): 355-375.
[24] GAO G W, YANG J, WU S S, et al. Bayesian Sample Steered Discriminative Regression for Biometric Image Classification. Applied Soft Computing, 2015, 37: 48-59.
[25] YANG W K, WANG Z Y, SUN C Y. A Collaborative Representation Based Projections Method for Feature Extraction. Pattern Recognition, 2015, 48(1): 20-27.
[26] XU J, XIE S L. Optimized Projections for Nonnegative Linear Reconstruction Classification. Neurocomputing, 2016, 173(3): 1743-1751.
[27] YIN J, LIU Z H, JIN Z, et al. Kernel Sparse Representation Based Classification. Neurocomputing, 2012, 77(1): 120-128.
[28] WANG Z Y, YANG W K, SHEN F M. Face Recognition Using a Low Rank Representation Based Projections Method. Neural Processing Letters, 2016, 43(3): 823-835.
[29] ZHAI Y K, GAN J Y, XU Y, et al. Fast Sparse Representation for Finger-Knuckle-Print Recognition Based on Smooth L0 Norm // Proc of the 11th IEEE International Conference on Signal Processing. Washington, USA: IEEE, 2012: 1587-1591.
[30] YANG W K, SUN C Y, WANG Z Y. Finger-Knuckle-Print Recognition Using Gabor Feature and MMDA. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 374-380.
[31] ZEINALI B, AYATOLLAHI A, KAKOOEI M. A Novel Method of Applying Directional Filter Bank(DFB) for Finger-Knuckle-Print(FKP) Recognition // Proc of the 22nd Iranian Conference on Electrical Engineering. Washington, USA: IEEE, 2014: 500-504.
[32] KUMAR M N B, PREMALATHA K. Finger Knuckle-Print Identification Based on Local and Global Feature Extraction Using Sdost. American Journal of Applied Sciences, 2014, 11(6): 929-938.
[33] KIM M K, FLYNN P J. Finger-Knuckle-Print Verification Based on Vector Consistency of Corresponding Interest Points // Proc of the IEEE Winter Conference on Applications of Computer Vision. Washington, USA: IEEE, 2014: 992-997.
[34] AOYAMA S, ITO K, AOKI T. A Finger-Knuckle-Print Recognition Algorithm Using Phase-Based Local Block Matching. Information Sciences, 2014, 268: 53-64.
[35] VERMA G, SINHA A. Finger Knuckle Print Based Verification Using Minimum Average Correlation Energy Filter. International Journal of Electronic Commerce Studies, 2014, 5(2): 233-246.
[36] MERAOUMIA A, CHITROUB S, BOURIDANE A. Multimodal Biometric Person Recognition System Based on Fingerprint & Finger-Knuckle-Print Using Correlation Filter Classifier // Proc of the IEEE International Conference on Communications. Washington, USA: IEEE, 2012: 820-824.
[37] HEMERY B, GIOT R, ROSENBERGER C. Sift Based Recognition of Finger Knuckle Print[C/OL]. [2016-07-25]. https://www.researchgate.net/publication/257365084.
[38] SULTHANA E S S, KANMANI S. Implementation and Evaluation of SIFT Descriptors Based Finger-Knuckle-Print Authentication System. Indian Journal of Science and Technology, 2014, 7(3): 374-382.
[39] ZHU L Q. Finger Knuckle Print Recognition Based on SURF Algorithm // Proc of the 8th International Conference on Fuzzy Systems and Knowledge Discovery. Washington, USA: IEEE, 2011: 1879-1883.
[40] MORALES A, TRAVIESO C M, FERRER M A, et al. Improved Finger-Knuckle-Print Authentication Based on Orientation Enhancement. Electronics Letters, 2011, 47(6): 380-381.
[41] MITTAL N, HANMANDLU M, VIJAY R. A Finger-Knuckle-Print Authentication System Based on DAISY Descriptor // Proc of the 12th International Conference on Intelligent Systems Design and Applications. Washington, USA: IEEE, 2012: 126-130.
[42] JAIN A, GUPTA R, HANMANDLU M. Finger Knuckle Print Based Authentication[C/OL]. [2016-07-25]. http://worldcomp-proceedings.com/proc/p2012/IPC3644.pdf.
[43] SHARIATMADAR Z S, FAEZ K. Finger-Knuckle-Print Recognition via Encoding Local-Binary-Pattern. Journal of Circuits, Systems, and Computers, 2013. DOI: 10.1142/S0218126613500503.
[44] QIAN J J, YANG J, GAO G W. Discriminative Histograms of Local Dominant Orientation(D-HLDO) for Biometric Image Feature Extraction. Pattern Recognition, 2013, 46(10): 2724-2739.
[45] EL-TARHOUNI W, SHAIKH M K, BOUBCHIR L, et al. Multi-scale Shift Local Binary Pattern Based-Descriptor for Finger-Knuckle-Print Recognition // Proc of the 26th International Conference on Microelectronics. Washington, USA: IEEE, 2014: 184-187.
[46] MERAOUMIA A, KORICHI M, CHITROUB S, et al. Finger-Knuckle-Print Identification Based on Histogram of Oriented Gradients and SVM Classifier // Proc of the 1st International Conference on New Technologies of Information and Communication. Washington, USA: IEEE, 2015. DOI: 10.1109/NTIC.2015.7368749.
[47] ZHANG L, ZHANG L, ZHANG D. Finger-Knuckle-Print: A New Biometric Identifier // Proc of the 16th IEEE International Conference on Image Processing. Washington, USA: IEEE, 2009: 1961-1964.
[48] ZHANG L, ZHANG L, ZHANG D. MonogenicCode: A Novel Fast Feature Coding Algorithm with Applications to Finger-Knuckle-Print Recognition // Proc of the International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics. Washington, USA: IEEE, 2010. DOI: 10.1109/ETCHE.2010.5559286.
[49] GAO G W, ZHANG L, YANG J, et al. Reconstruction Based Finger-Knuckle-Print Verification with Score Level Adaptive Binary Fusion. IEEE Transactions on Image Processing, 2013, 22(12): 5050-5062.
[50] KUMAR A, ZHOU Y. Personal Identification Using Finger Knuckle
Orientation Features. Electronics Letters, 2009, 45(20): 1023-1025.
[51] SHEN L L, JI Z, LI Y W, et al. Coding Gabor Features for Multi-modal Biometrics // Proc of the Chinese Conference on Pattern Recognition. Washington, USA: IEEE, 2010. DOI: 10.1109/CCPR.2010.5659155.
[52] MERAOUMIA A, CHITROUB S, BOURIDANE A. Personal Recognition by Finger-Knuckle-Print Based on Log-Gabor Filter Response // Proc of the International Conference on Electronics and Oil. Washington, USA: IEEE, 2011: 195-200.
[53] LI Z C, WANG K Q, ZUO W M. Finger-Knuckle-Print Recognition Using Local Orientation Feature Based on Steerable Filter // Proc of the International Conference on Intelligent Computing. Berlin, Germany: Springer, 2012: 224-230.
[54] BADRINATH G S, NIGAM A, GUPTA P. An Efficient Finger-Knuckle-Print Based Recognition System Fusing Sift and Surf Matching Scores // Proc of the 13th International Conference on Information and Communications Security. Berlin, Germany: Springer, 2011: 374-387.
[55] ZHANG L, ZHANG L, ZHANG D, et al. Phase Congruency Induced Local Features for Finger-Knuckle-Print Recognition. Pattern Recognition, 2012, 45(7): 2522-2531.
[56] GAO G W, YANG J, QIAN J J, et al. Integration of Multiple Orientation and Texture Information for Finger-Knuckle-Print Verification. Neurocomputing, 2014, 135: 180-191.
[57] GURU D S, SHANTHARAMU M. Feature Level Fusion of Multi-instance Finger Knuckle Print for Person Identification // Proc of the 1st International Conference on Intelligent Interactive Technologies and Multimedia. New York, USA: ACM, 2010: 186-190.
[58] KUMAR A, HANMANDLU M, GUPTA H M. Ant Colony Optimization Based Fuzzy Binary Decision Tree for Bimodal Hand Knuckle Verification System. Expert Systems with Applications, 2013, 40(2): 439-449.
[59] AMRAOUI M, ABOUCHABAKA J, EL AROUSSI M. Finger Knuckle
Print Recognition Based on Multi-instance Fusion of Local Feature Sets // Proc of the International Conference on Multimedia Computing and Systems. Washington, USA: IEEE, 2014: 87-92.
[60] GROVER J, HANMANDLU M. Hybrid Fusion of Score Level and Adaptive Fuzzy Decision Level Fusions for the Finger-Knuckle-Print Based Authentication. Applied Soft Computing, 2015, 31(C): 1-13.
[61] SHARIATMADARE Z S, FAEZ K. An Efficient Method for Finger-Knuckle-Print Recognition by Using the Information Fusion at Different Levels // Proc of the International Conference on Hand-Based Biometrics. Washington, USA: IEEE, 2011. DOI: 1.01109/ICHB.2011.6094325.
[62] MERAOUMIA A, CHITROUB S, BOURIDANE A. Robust Human Identity Identification System by Using Hand Biometric Traits // Proc of the 26th International Conference on Microelectronics. Washington, USA: IEEE, 2014: 17-20.
[63] WANG W M, HUANG X L, LIAO Q M. Fusion of Finger Vein and Finger Dorsal Texture for Personal Identification Based on Comparative Competitive Coding // Proc of the 19th IEEE International Conference on Image Processing. Washington, USA: IEEE, 2012: 1141-1144.
[64] THARWAT A, IBRAHIM A F, ALI H A. Multimodal Biometric Authentication Algorithm Using Ear and Finger Knuckle Images // Proc of the 7th International Conference on Computer Engineering & Systems. Washington, USA: IEEE, 2012: 176-179.
[65] MUTHUKUMAR A, KANNNAN S. K-means Based Multimodal Biometric Authentication Using Fingerprint and Finger Knuckle Print with Feature Level Fusion. Iranian Journal of Science and Techno-logy, 2013, 37(2): 133-145.
[66] PENG J L, LI Q, EL-LATIF A A A. Linear Discriminant Multi-set Canonical Correlations Analysis(LDMCCA): An Efficient Approach for Feature Fusion of Finger Biometrics. Multimedia Tools and Applications, 2015, 74(13): 4469-4486.
[67] PENG J L, LI Q, HAN Q, et al. Feature-Level Fusion of Finger Biometrics Based on Multi-set Canonical Correlation Analysis // Proc of the 8th Chinese Conference on Biometric Recognition. Berlin, Germany: Springer, 2013: 216-224.
[68] LI Y N, PENG J J, ZHONG Z, et al. A Multimodal Finger-Based Recognition Method Based on Granular Computing // Proc of the 9th Chinese Conference on Biometric Recognition. Berlin, Germany: Springer, 2014: 458-464.
[69] ESTHER R P, SHANMUGALAKSHMI R. Multimodal Biometric System Using Score Level Fusion of Palmprint and Finger Knuckle Print. International Journal of Advanced Research in Computer Science, 2014, 5(6): 182-185.
[70] NIGAM A, GUPTA P. Multimodal Personal Authentication Using Iris and Knuckleprint // Proc of the International Conference on Intelligent Computing Theory. Berlin, Germany: Springer, 2014: 819-825.
[71] NIGAM A, GUPTA P. Designing an Accurate Hand Biometric Based Authentication System Fusing Finger Knuckle Print and Palmprint. Neurocomputing, 2015, 151: 1120-1132.
[72] KHELLAT-KIHEL S, ABRISHAMBAF R, MONTEIRO J L, et al. Multimodal Fusion of the Finger Vein, Fingerprint and the Finger-Knuckle-Print Using Kernel Fisher Analysis. Applied Soft Computing, 2016, 42(1): 439-447.
[73] PERUMAL E, RAMACHANDRAN S. A Multimodal Biometric System Based on Palmprint and Finger Knuckle Print Recognition Methods. The International Arab Journal of Information Technology, 2015, 12(2): 118-128.
[74] ZHANG L, ZHANG L, ZHANG D. Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation // Proc of the 13th International Conference on Computer Analysis of Images and Patterns. Berlin, Germany: Springer, 2009: 141-148.
[75] ZHU L Q, ZHANG S Y. Multimodal Biometric Identification System Based on Finger Geometry, Knuckle Print and Palm Print. Pattern Recognition Letters, 2010, 31(2): 1641-1649.
[76] GUAN F X, WU Q Y, JIANG Z C, et al. Research of Dual-Model Recognition Algorithm Based on Finger Vein and Finger Crease // Proc of the 5th International Conference on Biomedical Engineering and Informatics. Washington, USA: IEEE, 2012: 358-361.
[77] LIU M, TIAN Y M, LI L H. A New Approach for Inner-Knuckle-Print Recognition. Journal of Visual Languages & Computing, 2014, 25(1): 33-42.
[78] SAVICˇ T, PAVESˇIC/ N. Personal Recognition Based on an Image of the Palmar Surface of the Hand. Pattern Recognition, 2007, 40(11): 3152-3163.
[79] SANCHES T. Hand Surface Biometrics for Personal Recognition. Master Dissertation. Lisbon, Portugal: Instituto Superior Técnico, 2008.
[80] ZHANG Y Q, SUN D M, QIU Z D. Hand-Based Feature Level Fusion for Single Sample Biometrics Recognition // Proc of the International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics. Washington, USA: IEEE, 2010. DOI: 10.1109/ETCHB.2010.5559289.
[81] ZHANG Y Q, SUN D M, QIU Z D. Hand-Based Single Sample Biometrics Recognition. Neural Computing and Applications, 2012, 21(8): 1835-1844.
[82] LIU F, ZHANG D, GUO Z H. Distal-Interphalangeal-Crease-Based User Authentication System. IEEE Transactions on Information Forensics and Security, 2013, 8(9): 1446-1455.
[83] HAO Y, TAN T N, SUN Z N, et al. Identity Verification by Using Handprint // Proc of the International Conference on Advances in Biometrics. Berlin, Germany: Springer, 2007: 328-337.
[84] MICHAEL G K O, CONNIE T, CHIN T C, et al. Realizing Hand-Based Biometrics Based on Visible and Infrared Imagery // Proc of the 17th International Conference on Neural Information Processing: Models and Applications. Berlin, Germany: Springer, 2010: 606-615.
[85] KUMAR A, ZHOU Y B. Human Identification Using Finger Images. IEEE Transactions on Image Processing, 2012, 21(4): 2228-2244.
[86] LIU M, TIAN Y M, MA Y H. Inner-Knuckle-Print Recognition Based on Improved LBP // Proc of the International Conference on Information Technology and Software Engineering. Berlin, Germany: Springer, 2013: 623-630.
[87] NANNI L, BRAHNAM S, LUMINI A. A User Dependent Multi-resolution Approach for Biometric Data. International Journal of Information Technology and Management, 2012, 11(1/2): 112-121.
[88] BHASKAR B, VELUCHAMY S. Hand Based Multibiometric Authentication Using Local Feature Extraction // Proc of the International Conference on Recent Trends in Information Technology. Washington, USA: IEEE, 2014. DOI: 10.1109/ICRTIT.2014.6996136.
[89] XU X M, LAI X Z, JIN Q, et al. A Novel IKP-Based Biometric Recognition Using Mobile Phone Camera. International Journal of Distributed Sensor Networks, 2015.DOI: 10.1155/2015/705710.
[90] FERRER M A, MORALES A, TRAVIESO C M, et al. Low Cost Multimodal Biometric Identification System Based on Hand Geometry, Palm and Finger Print Texture // Proc of the 41st Annual IEEE International Carnahan Conference on Security Technology. Washington, USA: IEEE, 2007: 52-58.
[91] NANNI L, LUMINI A. A Multi-matcher System Based on Knuckle-Based Features. Neural Computing and Applications, 2009, 18(1): 87-91.
[92] GOH M K O, CONNIE T, TEOH A B J. Bi-modal Palm Print and Knuckle Print Recognition System. Journal of IT in Asia, 2010, 3: 53-66.
[93] LI Q, QIU Z D. Handmetric Verification Based on Feature-Level Fusion. International Journal of Computer Science and Network Security, 2006, 6(2A): 164-168. |
|
|
|
2017, Vol. 30 
