A NEW FINGER-VEIN RECOGNITION SYSTEM USING THE COMPLETE LOCAL BINARY PATTERN AND THE PHASE ONLY CORRELATION
A new system for finger-vein recognition is proposed based on the Complete Local Binary pattern (CLBP) as afeature extractor and the Phase Only Correlation (POC) for post-processing alignment and for speeding up the system. The CLBP produces three components of image descriptors and thus holds more details compared to the previous methods such as the Local Binary Pattern (LBP), the Local Directional Pattern (LDP), the Local Line Binary Pattern (LLBP), the Repeated Line Tracking (RLT), the Maximum Curvature (MC) and the Wide Line Detector (WLD). In the proposed system, POC is used for two purposes. First, to increase the performance of the system the alignment between the CLBP components of the test image and the enrolled CLBP components are performed. Second, to speed up the matching stage, a portion of the enrolled images is excluded that are highly misaligned with the test image from the Hamming Distance (HD) measure competition in the matching stage. To make the system more secure against attacks targeting personal information, only CLBP components are enrolled in the system and the alignment process POC is implemented on these components without the need to original images. For image pre-processing a novel scheme of pre-processing methods is adopted including finger-vein localization, alignment, and the Region-Of-Interest (ROI) extraction and enhancement. Two databases, UTFVP and SDUMLA-HMT, are used to evaluate the performance of the system. The results have shown that the values for the Identification Recognition Rate (IRR) and the Equal Error Rate (EER) are respectively (99.66%) and (0.139) for the UTFVP database and (98.95%, and 0.53%) for SDUMLA-HMT database. These results are competitive compared to those achieved by the state-of-art systems.
E. C. Lee, H. Jung, and D. Kim,”New finger biometric method using near infrared imaging”, Sensors, Vol. 11, 2011, pp. 2319-2333.
S. Khellat-Kihel, R. Abrishambaf, N. Cardoso, J. Monteiro, and M. Benyettou, “Finger-vein recognition using Gabor filter and Support Vector Machine”, in International Image Processing, Applications and Systems Conference, IPAS 2014, 2014, pp. 1-6.
H. G. Hong, M. B. Lee, and K. R. Park,“Convolutional Neural Network-Based Finger-vein Recognition Using NIR Image”, Sensors, Vol. 17, 2017.
R. Das, E.Piciucco, E.Maiorana, and P.Campisi, “Convolutional Neural Network for Finger-vein based Biometric Identification”, in IEEE Transaction for Information Forensics and Security, Vol. 14, No. 2, 2019, pp. 360-373.
A. AK. Tahir, S.S. Dawood, and S. Anghelus, “An Iris Recognition System Using ANew Method of Iris Localization”, International Journal of Open Information Technologies, 2021, In press.
C. Otti, “Comparison of biometric identification methods”, IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), May 12-14, 2016, Timisoara, Romania, 2016, pp. 339-344.
K. Malik, and S. Bhattacharya, “Comparative Study of Different Biometric Features”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 7, 2013, pp. 2776-2784.
R. Saini, N. Rana, “Comparison of Various Biometric Methods”, International Journal of Advances in Science and Technology (IJAST), Vol. 2, No. 1, 2014, pp. 24-30.
A.AK. Tahir, and S. Anghelus, “Human Biometrics and Biometric Recognition Systems; An Overview”, A XIX-a International Conference on Multidisciplinary, "Professor Dorin Paul - Romanian hydropower founder", Vol. 35/2019, pp. 431-446.
A. A. Tahir, and S. Anghelus, “An accurate and fast method for eyelid detection”, International Journal of Biometrics, Vol. 12, No. 2, 2020, pp. 163-178.
D. Wang, J. Li, and G. Memik, “User Identification based on Finger-vein Patterns for Consumer Electronics Devices”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, 2010, pp. 799-804.
J. Yang, J. Wei, and Y. Shi, “Accurate ROI Localization and Hierarchical Hyper-sphere Model for Finger-vein Recognition”, Neurocomputing, 2018.
N. Miura, A.Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification”, Machine Vision and Applications, Vol. 15, 2004, pp. 194–203.
N. Miura, A.Nagasaka, and T. Miyatake, “Extraction of finger-vein patterns using maximum curvature points in image profiles”, IEICE Transactions on Information and Systems, Vol. 90, No. 8, 2007, pp. 1185–1194.
W. Song, T. Kim, H. C. Kim, K. R. PARK,“A finger-vein verification system using mean curvature”, Pattern Recognition Letters, Vol. 32, 2011, pp. 1541-1647.
E.C. Lee, H. C. Lee, and K. P. Park, “Finger-vein Recognition Using Minutia-Based Alignment and Local Binary Pattern-Based Feature Extraction”, International Journal of Imaging Systems and Technology, Vol. 9, No. 3, 2009, pp. 179-186.
H.C. Lee, B.J. Kang, E.C. Lee, and K. R. Park, “Finger-vein recognition using weighted local binary pattern code based on a support vector machine”, Journal of Zhejiang University SCIENCE C (Computer & Electronics), Vol. 11, No. 7, 2010, pp. 514-524.
K. R. Park, “Finger-vein Recognition By Combining Global And Local Features Based On SVM”, Computing and Informatics, Vol. 30, No. 2, 2011, pp. 295–309.
B. A. Rosdi, W.S. Chai, and A. A. Shahrel, “Finger-vein Recognition Using Local Line Binary Pattern”, Sensors, Vol. 11, 2011, pp. 11357-11371.
B. Huang, Y. Dai, R. Li, D. Tang, and W. Li, “Finger-vein Authentication Based On Wide Line Detector And Pattern Normalization”, in Proceedings - International Conference on Pattern Recognition, 2010, pp. 1269-1272.
Y. Lu, S. J. Xie, S.Yoon, D. S. Park, “Finger-vein Identification Using Polydirectional Local Line Binary Pattern”, International Conference on ICT Convergence (ICTC), 2013, pp. 61-65.
H. Liu, L. Song, G. Yang, L. Yang, and Y. Yin, “Customized Local Line Binary Pattern Method for Finger-vein Recognition”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer US, 2017, pp. 314–323.
J. F. Yang, J. L. Yang, Y. H. Shi, “Finger-vein recognition based on a bank of Gabor filters”, In: Proceedings of ACCV’09, 2009, pp. 374–383.
Y. Lu, S. Yoon, and D. S. Park, “Finger-vein Recognition based on Matching Score-Level Fusion of Gabor Features”, The Journal of Korean Institute of Communications and Information Sciences, Vol. 38A, No. 2, 2013, pp. 174-182.
J. Peng, Q. Li, N. Wang, A. A. Abd El-Latif, and X. Niu, “An Effective Preprocessing Method for Finger-vein Recognition”, Fifth International Conference on Digital Image Processing (ICDIP 2013), 2013, edited by Yulin Wang, Xie Yi, Proc. of SPIE, Vol. 8878.
M. Kaur, G. Babbar, and C. E. C.Landran, “Finger-vein Detection using Repeated Line Tracking , Even Gabor and Multilinear Discriminant Analysis ( MDA )’, Vol. 6, No. 4, 2015, pp. 3280–3284.
Y. H. Yahaya, S. M. Shamsuddin, and W. Y. Leng, “Finger-vein Feature Extraction Using Discretization”, 4th International Conference on Artificial Intelligence and Computer Science, 2016.
S. Brindha, “Finger-vein recognition”, International Research Journal of Engineering and Technology (IRJET), Vol. 4, No. 4, 2017, pp. 1298-1300.
M. A. Syarif, T. S. Ong, A. B. J. Teoh, and C. Tee,“Enhanced Maximum Curvature Descriptors for Finger-vein Verification”, Multimedia Tools and Applications, Vol. 76, No. 5, 2017, pp. 6859-6887.
Y. Lu, S. Yoon, S. Wu, and D. S. Park, “Pyramid Histogram of Double Competitive Pattern for Finger-vein Recognition”, IEEE Acess, Vol. 6, 2018, pp. 56445–56456.
X. Meng, X. Xi, G. Yang, et al., “Finger-vein recognition based on deformation information”, SCIENCE CHINA, Information Sciences, Vol. 61, 2018.
H. Wang, M. Du, J. Zhou, and L. Tao, “Weber Local Descriptors with Variable Curvature Gabor Filter for Finger-vein Recognition”, IEEE Acess, Vol. 7, 2019, pp. 108261-108277.
S. Tang, S. Zhou, W. Kang, Q. Wu, and F. Deng, “Finger-vein Verification using a Siamese Convolutional Neural Network”, IET Biometrics, 2019.
A. I. Mohammed, and A. AK. Tahir, A. AK., “A New Image Classification System Using Deep Convolution Neural Network And Modified Amsgrad Optimizer”, Journal of University of Duhok (Pure and Eng. Science), Vol. 22, No.2, 2019, pp. 89-101.
A. I. Mohammed, and A. AK. Tahir, “A New Optimizer for Image Classification using Wide ResNet (WRN)”, Academic Journal of Nawroz University, Vol. 9, No. 4, 2020, pp. 1-13.
C. Kauba, J.Reissig, J. and A. Uhl, “Pre-processing cascades and fusion in finger-vein recognition”, In Proceedings of the International Conference of the Biometrics Special Interest Group, 2014, pp. 99-110.
K. Q. Wang, A. S.Krisa, X. Q. Wu,and Q. S. Zhao, “Finger vein recognition using LBP variance with global matching”, Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, 2012, pp. 196-200.
A. A. Mustafa, and A. AK. Tahir,“Improving the Performance of Finger vein Recognition System Using A New Scheme of Modified Preprocessing Methods”, Academic Journal of Nawroz University, Vol. 9, No. 3, 2020, pp. 397- 409.
Z. Guo, Z. Lei, and Z. David, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”, IEEE Transactions on Image Processing, Vol. 19, No. 6, 2010, pp. 1657-1663.
Y. Zhao, D. Huang, and W. Jia, “Completed Local Binary Count for Rotation Invariant Texture Classification”, IEEE Transactions On Image Processing, Vol. 21, No. 10, 2012, pp, 4492-4497.
T. H. Rassem and B. E. Khoo, “Completed Local Ternary Pattern for Rotation Invariant Texture Classification”, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2014, Article ID 373254, pp.1-10.
M. Guermoui, and M. L. Mekhalfi, “A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition”, 2016, pp.1-4.
S. NagarajaC. J. Prabhakar and P. U. Kumar, “Complete Local Binary Pattern for Representation of Facial Expression Based on Curvelet Transform”, Proceeding of International Conference on Multimedia Processing, Communication & Info. Tech., MPCIT, 2013, Association of Computer Electronics and Electrical Engineers, E, DOI: 03.AETS.2013.4.32.
K. Takita, T. Aoki, Y. Sasaki, T. Higuchi, and K. Kobayashi, “High Accuracy Subpixel image registration based on Phase-Only-Correlation”, IEICE Transaction on Fundamentals, Vol. E86-A, No. 8, 2003, pp. 1925-1934.
K. Ito, H. Nakajima, K. Kobayashi, T. Aoki, and T. Higuchi,“A Fingerprint Matching Algorithm Using Phase Only Correlation”, IEICE Transaction on Fundamentals, Vol. E87-A, No. 3, 2004, pp. 682-691.
K. Ito, T. Aoki, H. Nakajima, K. Kobayashi, and T. Higuchi, “A Palmprint Recognition Algorithm Using Phase Only Correlation”, IEICE Transaction on Fundamentals, Vol. E91-A, No. 4, 2008, pp. 1023-1030.
M. Miura, S. Sakai, S. Aoyama, et al., “High-Accuracy Image Matching Using Phase-Only Correlation and Its Application”, SICE Annual Conference, 2012, pp. 307-312.
M. Vanoni, P. Tome, L. El Shafey, and S. Marcel, “Cross-Database Evaluation Using an Open Finger-vein Sensor”, IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings, Rome, 2014, pp. 30-35.
Y. Yin L. Liu and X. Sun, “SDUMLA-HMT: A multimodal Biometric Database”, In Biometric Recognition by (Sun, Z., L., J., Chen, X., Tan, T. (Eds.)), Springer Berlin Heidelberg, 2011, pp. 260-268.
Y. Lu, S. J.Xie, S. Yoon, Z. Wang, and D. S. Park, “An Available Database for the Research of Finger-vein Recognition”, the 6th International Congress on Image and Signal Processing, 2013, pp. 410-415.
C. Kauba, and A. Uhl, “Sensor Ageing Impact On Finger-vein Recognition”, International Conference on Biometrics (ICB), Phuket, 2015, pp. 113-120.
S. J. Xie, Y. Lu, S. Yoon, J. Yang, and D. S> Park, “Intensity Variation Normalization for Finger-vein Recognition Using Guided Filter Based Singe Scale Retinex”, Sensors, Vol. 15, 2015, pp. 17089-17105.
C. Kauba, A. Uhl, E.Piciucco, E. Maiorana, and P. Campisi, “Advanced variants of feature level fusion for finger-vein recognition”, In the International Conference of the Biometrics Special Interest Group (BIOSIG), 2016, pp. 1–7.
K. Y. Shin, Y. H. Park, D. T. Nguyen, and K. R. Park, “Finger-vein Image Enhancement Using a Fuzzy-Based Fusion Method with Gabor and Retinex Filtering”, Sensors, Vol. 14, 2014, pp. 3095-3129.
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