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The Finger-vein recognition (FVR) method has received increasing attention in recent years. It is a new method of personal identification and biometric technology that identifies individuals using unique finger-vein patterns, which is the first reliable and suitable area to be recognized. It was discovered for the first time with a home imaging system; it is characterized by high accuracy and high processing speed. Also, the presence of patterns of veins inside one’s body makes it almost difficult to repeat and difficult to steal. Based on the increased focus on protecting privacy, that also produces vein biometrics safer alternatives without forgery, damage, or alteration over time. Fingerprint recognition is beneficial because it includes the use of low-cost, small devices which are difficult to counterfeit. This paper discusses preceding finger-vein recognition approaches systems with the methodologies taken from other researchers’ work about image acquisition, pretreatment, vein extraction, and matching. It is reviewing the latest algorithms; continues to critically review the strengths and weaknesses of these methods, and it states the modern results following a key comparative analysis of methods.
Wydawca
Rocznik
Tom
Strony
36--46
Opis fizyczny
Bibliogr. 68 poz., fig., tab.
Twórcy
autor
- Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq
autor
- Corresponding author’s e-mail: ruaa.salman1201@sc.uobaghdad.edu.iq
Bibliografia
- 1. Wu J.D., Ye S.H. Driver identification using fingervein patterns with Radon transform and neural network. Expert Syst. Appl. 2009; 36(3): 5793–5799.
- 2. Yang J., Shi Y., Jia G. Finger-vein image matching based on adaptive curve transformation. Pattern Recognit. 2017; 66: 34–43.
- 3. Hashimoto J. Finger vein authentication technology and its future. In 2006 Symposium on VLSI Circuits, 2006. Digest of Technical Papers. 2006, 5–8.
- 4. Wang L., Leedham G., Cho D.S. Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recognit. 2008; 41(3): 920–929.
- 5. Miura N., Nagasaka A., Miyatake T. Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans. Inf. Syst. 2007; 90(8): 1185–1194.
- 6. Shahin M., Badawi A., Kamel M. Biometric authentication using fast correlation of near infrared hand vein patterns. Int. J. Biol. Med. Sci. 2007; 2(3): 141–148.
- 7. Al-Tamimi M S H. A survey on the vein biometric recognition systems: Trends and challenges. Journal of Theoretical and Applied Information Technology, 2019, 97(2), 551–568.
- 8. Aboalsamh H.A. Vein and fingerprint biometrics authentication-future trends. Int. J. Comput. Commun. 2009; 3(4): 67–75.
- 9. Wang K., Liu J. Finger vein recognition method based relative distance and angle,” Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal Huazhong Univ. Sci. Technol. Natural Sci. Ed. 2011; 39(5), 96–99. , DOI: 10.5772/18025.
- 10. Tagkalakis F., Vlachakis D., Megalooikonomou V., Skodras A. A novel approach to finger vein authentication,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). 2017; 659–662.
- 11. Lu Y., Wu S., Fang Z., Xiong N., Yoon S., Park D. S.Exploring finger vein based personal authentication for secure IoT. Futur. Gener. Comput. Syst. 2017; 77: 149–160.
- 12. Sun X., Lin C.Y., Li M.Z., Lin H.W., Chen Q.W., A DSP-based finger vein authentication system,” in 2011 Fourth International Conference on Intelligent Computation Technology and Automation. 2011; 2: 333–336.
- 13. Liu Z., Song S. An embedded real-time fingervein recognition system for mobile devices,” IEEE Trans. Consum. Electron. 2012; 58(2): 522–527.
- 14. Li Z., Nagasaka A., Kurihara T., Kiyomizu H., Kagehiro T. A hybrid biometric system using touch-panel-based finger-vein identification and deformable-registration-based face identification,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014; 69–74.
- 15. Khellat-kihel S., Cardoso N., Monteiro J., Benyettou M. Finger vein recognition using Gabor filter and Support Vector Machine,” in International image processing, applications and systems conference. 2014; 1–6.
- 16. Yang L., Yang G., Yin Y., Xiao R. Finger vein image quality evaluation using support vector machines. Opt. Eng. 2013; 52(2): 27003.
- 17. Finger vein identification using polydirectional local line binary pattern. In 2013 International Conference on ICT Convergence (ICTC). 2013; 61–65.
- 18. Sapkale M., Rajbhoj S. M. A biometric authentication system based on finger vein recognition,” in 2016 International Conference on Inventive Computation Technologies (ICICT). 2016; 3: 1–4.
- 19. Wilson C. Vein pattern recognition: a privacy-enhancing biometric. CRC press, 2010.
- 20. Yang J., Shi Y., Yang J. Personal identification based on finger-vein features,” Comput. Human Behav. 2011; 27(5): 1565–1570.
- 21. Wang K.Q.,. Khisa A.S, Wu X.Q., Zhao Q.S. Finger vein recognition using LBP variance with global matching,” in 2012 international conference on wavelet analysis and pattern recognition. 2012; 196–201.
- 22. Ratha N.K., Govindaraju V. Advances in biometrics: sensors, algorithms and systems. Springer Science & Business Media, 2007.
- 23. Matsuda Y., Miura N., Nagasaka A., Kiyomizu H., Miyatake T. Finger-vein authentication based on deformation-tolerant feature-point matching. Mach. Vis. Appl. 2016; 27(2): 237–250.
- 24. Liu Z., Yin Y., Wang H., Song S., Li Q. Finger vein recognition with manifold learning. J. Netw. Comput. Appl. 2010; 33(3): 275–282.
- 25. Damavandinejadmonfared S., Mobarakeh A.K., Pashna M., Gou J., Rizi S.M., Nazari S., Khaniabadi S.M., Bagheri M.A. Finger vein recognition using PCA-based methods. World Academy of Science, Engineering and Technology. 2012; 25: 66.
- 26. Rosdi B.A., Shing C.W., Suandi S.A. Finger vein recognition using local line binary pattern. Sensors. 2011; 11(12): 11357-11371.
- 27. Meng X., Yang G., Yin Y., Xiao R. Finger vein recognition based on local directional code. Sensors. 2012; 12(11): 14937-14952.
- 28. Wang H., Du M., Zhou J., Tao L. Weber local descriptors with variable curvature gabor filter for finger vein recognition. IEEE Access. 2019; 12(7): 108261–108277.
- 29. Song W., Kim T., Kim H.C., Choi J.H., Kong H.J., Lee S.R. A finger-vein verification system using mean curvature. Pattern Recognition Letters. 2011; 32(11): 1541–1547.
- 30. Kumar A., Zhou Y. Human identification using finger images. IEEE Transactions on image processing. 2011; 21(4): 2228–2244.
- 31. Liu C., Kim Y.H. An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE 2016, 3141–3145.
- 32. Davis V., Devane S. Diagnosis of Brain Hemorrhage Using Artificial Neural Network. International Journal of Scientific Research in Network Security and Communication. 2017; 5(1): 20–23.
- 33. Radzi S.A., Hani M.K., Bakhteri R. Finger-vein biometric identification using convolutional neural network. Turkish Journal of Electrical Engineering & Computer Sciences. 2016; 24(3): 1863–1878.
- 34. Tang D., Huang B., Li W., Li X. A method of evolving finger vein template. In2012 International Symposium on Biometrics and Security Technologies. IEEE 2012, 96–101.
- 35. Xin Y., Liu Z., Zhang H., Zhang H. Finger vein verification system based on sparse representation. Applied optics. 2012; 51(25): 6252–6258.
- 36. Jagadiswary D., Saraswady D. Biometric authentication using fused multimodal biometric. Procedia Computer Science. 2016; 1(85): 109–116.
- 37. Yang G., Xi X., Yin Y. Finger vein recognition based on a personalized best bit map. Sensors. 2012; 12(2): 1738–1757.
- 38. Lu Y., Yoon S., Xie S.J., Yang J., Wang Z., Park D.S. Finger vein recognition using generalized local line binary pattern. KSII Transactions on Internet and Information Systems (TIIS). 2014; 8(5): 1766–1784.
- 39. Wu J.D., Liu C.T. Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Systems with Applications. 2011; 38(5): 5423–5427.
- 40. Huang H., Liu S., Zheng H., Ni L., Zhang Y., Li W. DeepVein: Novel finger vein verification methods based on deep convolutional neural networks. In 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). IEEE 2017, 1–8.
- 41. Xie C., Kumar A. Finger vein identification using convolutional neural network and supervised discrete hashing. Deep Learning for Biometrics. Springer, Cham. 2017; 109–132.
- 42. Fang Y., Wu Q., Kang W. A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing. 2018; 17(290): 100–107.
- 43. Vega A.P., Travieso C.M., Alonso J.B. Biometric personal identification system based on patterns created by finger veins. In3rd IEEE International Work-Conference on Bioinspired Intelligence, IEEE 2014, 65-70.
- 44. Vlachos M., Dermatas E. Finger vein segmentation from infrared images based on a modified separable mumford shah model and local entropy thresholding. Computational and mathematical methods in medicine. 2015; 18.
- 45. Liu H., Song L., Yang G., Yang L., Yin Y. Customized local line binary pattern method for finger vein recognition. InC hinese Conference on Biometric Recognition. Springer, Cham. 2017; 28: 314–323.
- 46. Park K.R. Finger vein recognition by combining global and local features based on SVM. Computing and Informatics. 2011; 30(2): 295–309.
- 47. Wu J.D., Liu C.T. Finger-vein pattern identification using SVM and neural network technique. Expert Systems with Applications. 2011; 38(11): 14284–14289.
- 48. Veluchamy S., Karlmarx L.R. System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier. IET Biometrics. 2017; 6(3): 232–242.
- 49. Khanam R., Khan R., Ranjan R. Analysis of finger vein feature extraction and recognition using DA and KNN methods. In 2019 Amity international conference on artificial intelligence (AICAI). IEEE 2019, 477–483.
- 50. Rosdi B.A., Mukahar N., Han N.T. Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier. International Journal of Integrated Engineering. 2021; 13(1): 177–187.
- 51. Qin H., El-Yacoubi M.A. Deep representationbased feature extraction and recovering for fingervein verification. IEEE Transactions on Information Forensics and Security. 2017; 12(8): 1816–1829.
- 52. Qin H., El-Yacoubi M.A. Deep representation for finger-vein image-quality assessment. IEEE Transactions on Circuits and Systems for Video Technology. 2017; 28(8): 1677–1693.
- 53. Das R., Piciucco E., Maiorana E., Campisi P. Convolutional neural network for finger-veinbased biometric identification. IEEE Transactions on Information Forensics and Security. 2018; 14(2): 360–373.
- 54. Avcı A., Kocakulak M., Acır N. Convolutional Neural Network Designs for Finger-vein-based Biometric Identification. In2019 11th International Conference on Electrical and Electronics Engineering (ELECO) IEEE 2019, 580–584.
- 55. Scholar A. Minimization of Training Time of a Convolutional Neural Network by Adding K-Neareaset Neighbor as Classifier 1Prof. Souley Boukari; 2Fatima Ahmed Abubakar; 2Atika Ahmad Jibrin; 2Yakubu Nuhu Danjuma; 2020.
- 56. Zhao D., Ma H., Yang Z., Li J., Tian W. Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization. Infrared Physics & Technology. 2020; 105: 103221.
- 57. Lu Y., Xie S.J., Yoon S., Wang Z., Park D.S. An available database for the research of finger vein recognition. In 2013 6th International congress on image and signal processing (CISP). IEEE 2013; 1: 410–415.
- 58. Huang B., Dai Y., Li R., Tang D., Li W. Fingervein authentication based on wide line detector and pattern normalization. In 2010 20th international conference on pattern recognition. IEEE 2010, 1269–1272.
- 59. Yang W., Huang X., Zhou F., Liao Q. Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion. Information sciences. 2014; 1(268): 20–32.
- 60. Asaari M.S., Suandi S.A., Rosdi B.A. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Systems with Applications. 2014; 41(7): 3367–3382.
- 61. Ton B.T., Veldhuis R.N. A high quality finger vascular pattern dataset collected using a custom designed capturing device. In 2013 International conference on biometrics (ICB) IEEE 2013, 1–5.
- 62. Yin Y., Liu L., Sun X. SDUMLA-HMT: a multimodal biometric database,” in Chinese Conference on Biometric Recognition, 2011, 260–268.
- 63. Tome P., Vanoni M., Marcel S. On the vulnerability of finger vein recognition to spoofing. In2014 International Conference of the Biometrics Special Interest Group (BIOSIG) IEEE 2014, 1–10.
- 64. William A., Ong T.S., Tee C., Goh M.K. Multi-instance finger vein recognition using local hybrid binary gradient contour. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) IEEE 2015, 1226–1231.
- 65. Qiu S., Liu Y., Zhou Y., Huang J., Nie Y. Fingervein recognition based on dual-sliding window localization and pseudo-elliptical transformer. Expert Systems with Applications. 2016; 1(64): 618–632.
- 66. Mukahar N., Rosdi B.A. Interval valued fuzzy sets k-nearest neighbors classifier for finger vein recognition. InJournal of Physics: Conference Series. IOP Publishing. 2017; 890(1): 012069.
- 67. Shazeeda S., Rosdi B.A. Nearest centroid neighbor based sparse representation classification for finger vein recognition. IEEE Access. 2018; 24(7): 5874–5885.
- 68. Shazeeda S., Rosdi B.A. Finger vein recognition using mutual sparse representation classification. IET Biometrics. 2019; 8(1): 49–58.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-56df1b38-f41f-4eb7-bc2b-2eae62038d65