BIOMETRIC VERIFICATION SYSTEM BASED ON HAND GEOMETRY
DOI:
https://doi.org/10.4314/jfas.v13i2.11Keywords:
Biometric; hand geometry; length and width; verification systemAbstract
Biometric systems are widely used in medium and low security applications. Verification systems based on the geometry of the hand utilize some geometrical characteristics of the hand including measurements of fingers, shape of the palm, etc. In this work, we have developed an unconstrained and contact-based hand geometry verification system, using a combination of length and width of fingers. New measurements at different points of fingers were introduced in this paper to improve the performance of the recognition of persons. A total of 135 hand images were enrolled in this study. The Euclidean distance was used as a similarity function for different values of threshold. The proposed method was compared to state-of-the-art approaches. The results obtained reveal the high performance of the proposed approach and outperformed the existing methods with an accuracy of Acc = 98.67%.
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References
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[27] Sanchez-Reillo R, Sanchez-Avila C, Gonzalez-Marcos A. Biometric identification through hand geometry measurements, IEEE T. Pattern Anal., 22(10), 2000, 1168–1171, doi: 10.1109/34.879796
[28] Wong L, Shi P. Peg-free hand geometry recognition using hierarchical geometry and shape matching, in Proceedings of the IAPR Workshop on Machine Vision Applications, Nara, Japan, 2002, 281–284.
[29] Morales A, Ferrer M, Díaz F, Alonso J, Travieso C. Contact-free hand biometric system for real environments, in: Proceedings of the 16th European Signal Processing Conference (EUSIPCO), Laussane, Switzerland, 2008.
[30] Aundez-Zanuy M. Biometric verification of humans by means of hand geometry. In: 39th Annual 2005 International Carnahan Conference on Security Technology CCST, 2005, 61–67, doi: 10.1109/CCST.2005.1594816
[31] Harrar K, Khider M. Texture analysis using multifractal spectrum, International Journal of Modeling and Optimization 4 (4), 2014, 336-341, doi: 10.7763/IJMO.2014.V4.396
[2] Sharma S, Dubey S R, Singh S K, Saxena R, Singh R K. Identity verification using shape and geometry of human hands, Expert Sys. Appl., 2015, 42(2), 821-832, doi: 10.1016/j.eswa.2014.08.052
[3] Miroslav B, Grd P, Fotak T. Basic Principles and Trends in Hand Geometry and Hand Shape Biometrics, Article. Intech, 2012, pp. 77-99, doi: 10.5772/51912
[4] Klonowski M, Plata M, Syga P. User authorization based on hand geometry without special equipment, Pattern Recogn., 73, 2018, 189-201, doi: 10.1016/j.patcog.2017.08.017
[5] Barra S, Marsico M D, Nappi M, Narducci P, Riccio D. A hand-based biometric system in visible light for mobile environments, Inform. Sciences, 479, 2019, 472-485, doi: 10.1016/j.ins.2018.01.010
[6] Firas M, Salih Z. Authentication system depends on hand geometry using backpropagation neural network, JITBM & ARF. 2014, Université Polytechnique Iraq
[7] Burgues J, Fierrez J, Ramos D, Ortega-Garcia J. Comparison of Distance-Based Features for Hand Geometry Authentication. In: Fierrez J, Ortega-Garcia J, Esposito A, Drygajlo A, Faundez-Zanuy M (eds) Biometric ID Management and Multimodal Communication. BioID 2009. Lecture Notes in Computer Science, 5707. Springer, Berlin, Heidelberg, 2009
[8] Gross R, Li Y, Sweeney L, Jiang X, Xu W, Yurovsky D. Robust Hand Geometry Measurements for Person Identification using Active Appearance Models, IEEE, 2007, 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, doi: 10.1109/BTAS.2007.4401936
[9] Goh M K O, Connie T, Teoh A B J. A contactless biometric system using multiple hand features, J. Vis. Commun. Image R., 23(7), 2012, 1068-1084, doi: 10.1016/j.jvcir.2012.07.004
[10] Shawkat S A, Lateef Al-Badri K S, Turki A I. The new hand geometry system and automatic identification, Period. Eng. Nat. Sci., 7(3), 2019, 996-1008, doi: 10.21533/pen.v7i3.632
[11] Appiah O, Asante A, Hayfron-Acquah J B. Improved approximated median filter algorithm for real-time computer vision applications, J. King Saud Univ., Comp. & Info. Sci., 2020, doi: 10.1016/j.jksuci.2020.04.005
[12] Singh R, Vatsa M, Noore A. Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model, Signal Processing, 87(11), 2007, 2746-2764, doi: 10.1016/j.sigpro.2007.05.009
[13] Otsu N. A threshold selection method from gray-level histograms, IEEE Trans. Sys., Man., Cyber., 9, 1979, 62–66, doi: 10.1109/TSMC.1979.4310076
[14] Gonzalez R C, Woods R E, Eddins S L. Digital Image Processing Using MATLAB, New Jersey, Pearson Prentice Hall, 2004.
[15] Matos H, Oliveira H.P, Magalhães F. Hand-Geometry Based Recognition System. In: Campilho A., Kamel M. (eds) Image Analysis and Recognition, ICIAR 2012, Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg, doi: 10.1007/978-3-642-31298-4_5
[16] Ahuja M K, Singh A. (2015, Mar). A survey of hand geometry recognition. Ijarcsms. 3(3), 2015, 312-318
[17] Kolountzakis M N, Kutulakos K N, Fast computation of the Euclidian distance maps for binary images, Inform. Process. Lett., 43(4), 1992, 181-184, doi: 0.1016/0020-0190(92)90197-4
[18] Davydov V A. On Application of the Modulus Metric to Solving the Minimum Euclidean Distance Decoding Problem, Probl. Inf. Transm. 55, 2019, 145–151, doi: 10.1134/S0032946019020030
[19] Ohzeki K, Takatsuka M, Kajihara M. Hirakawa Y, Sato K. On the false rejection ratio of face recognition based on automatic detected feature points, Pattern Recognit. Image Anal. 26, 2016, 379–384, doi: 10.1134/S1054661816020073
[20] Ali S S, Baghel V S, Ganapathi I I, Prakash S. Robust biometric authentication system with a secure user template, Image Vision Comput., 104, 2020, doi: 10.1016/j.imavis.2020.104004
[21] Slooten K. Likelihood ratio distributions and the (ir) relevance of error rates, Forensic Sci. Int-Gen., 44, 2020, doi: 10.1016/j.fsigen.2019.102173
[22] Bapat A, Kanhangad V. Segmentation of hand from cluttered backgrounds for hand geometry biometrics, IEEE Region 10 Symposium, 2017, doi: 10.1109/TENCONSPRING.2017.8070016
[23] Bulatov Y, Jambawalikar S, Kumar P, Sethia S. Hand recognition using geometric classifiers, Lect. Notes Comput. Sc., 3072, 2004, 753-760, doi: 10.1007/978-3-540-25948-0_102
[24] Muthukumar A, Kavipriya A. A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print, Pattern Recogn. Lett., 125, 2019, 150-156, doi: 10.1016/j.patrec.2019.04.007
[25] Hanley J A, McNeill B J. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve, Radiol., 143 (1), 1982, 29–36, doi: 10.1148/radiology.143.1.7063747
[26] Harrar K, Jennane R, Trabecular texture analysis using fractal metrics for bone fragility assessment, structure, 9(9), 2015, 683-688, doi: 10.5281/zenodo.1108242
[27] Sanchez-Reillo R, Sanchez-Avila C, Gonzalez-Marcos A. Biometric identification through hand geometry measurements, IEEE T. Pattern Anal., 22(10), 2000, 1168–1171, doi: 10.1109/34.879796
[28] Wong L, Shi P. Peg-free hand geometry recognition using hierarchical geometry and shape matching, in Proceedings of the IAPR Workshop on Machine Vision Applications, Nara, Japan, 2002, 281–284.
[29] Morales A, Ferrer M, Díaz F, Alonso J, Travieso C. Contact-free hand biometric system for real environments, in: Proceedings of the 16th European Signal Processing Conference (EUSIPCO), Laussane, Switzerland, 2008.
[30] Aundez-Zanuy M. Biometric verification of humans by means of hand geometry. In: 39th Annual 2005 International Carnahan Conference on Security Technology CCST, 2005, 61–67, doi: 10.1109/CCST.2005.1594816
[31] Harrar K, Khider M. Texture analysis using multifractal spectrum, International Journal of Modeling and Optimization 4 (4), 2014, 336-341, doi: 10.7763/IJMO.2014.V4.396
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Published
2021-01-15
How to Cite
HARRAR, K. BIOMETRIC VERIFICATION SYSTEM BASED ON HAND GEOMETRY. Journal of Fundamental and Applied Sciences, [S. l.], v. 13, n. 2, p. 816–844, 2021. DOI: 10.4314/jfas.v13i2.11. Disponível em: https://jfas.info/index.php/JFAS/article/view/999. Acesso em: 30 jan. 2025.
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