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A new approach to biometric recognition based on hand geometry

Published: 13 April 2015 Publication History

Abstract

In the past years recognition by biometric information has been increasingly adopted. This paper presents a new approach to biometric recognition based on hand geometry. A database with 100 individuals and samples of both sides of the hands was used. The process prioritizes user comfort during capture and produces segmentation of hands and fingers with high precision. Altogether, 54 features have been extracted and different classification and training methods were evaluated. Tests using cross-validation and stratified random subsampling techniques were performed. The experiments demonstrated competitive results when compared to other state-of-the-art methods. The proposed approach obtained 100% accuracy using the Logist Boost together with Random Forest learning strategy and Bagging together with FLR combination.

References

[1]
Aha, D. W., Kibler, D. and Albert, M. K. Instance-based learning algorithms. Machine Learning, 6, 1 (1991), 37--66.
[2]
Bouckaert, R. R. Bayesian Network Classifiers in Weka for Version 3-5-7. University of Waikato. 2008.
[3]
Breiman, L. Bagging predictors. Machine Learning, 24, 2 (1996), 123--140.
[4]
Breiman, L. Random Forests. Machine Learning, 45, 1 (2011), 5--32.
[5]
Chang, Chih-Chung and Lin, Chih-Jen. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 3 (April 2011).
[6]
de Santos-Sierra, A, Sanchez--Vila, C., del Pozo, G. B., and Guerra-Casanova, J. Unconstrained and contactless hand geometry biometrics. Sensors, 11 (2011), 10143--10164.
[7]
Ernst, R. H. Hand ID system. US Patent, n°. 3576537, 1971.
[8]
Faundez-Zanuy, Marcos and Mérida, Guilhermo M. N. Biometric Identification by Means of Hand Geometry and a Neural Net Classifier. In Computational Intelligence and Bioinspired Systems. Springer Berlin Heidelberg, 2005.
[9]
Freund, Y. and Schapire, R. E. Experiments with a new boosting algorithm. Thirteenth International Conference on Machine Learning (1996), 148--156.
[10]
Friedman, J., Hastie, T. and Tibshirani R. Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics, 28 (1998).
[11]
Gonzalez, R. C., Woods, R. E. and Eddins S. L. Digital Image Processing using Matlab. Person Prentice Hall, 2004.
[12]
Guo, J., Hsia C., Liu Y., Chu, M. and Le, T. Contact-free hand geometry-based identification system. Expert Systems with Applications, 39 (Oct 2012), 11728--11736.
[13]
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten I. H. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11 (2009).
[14]
Haralick, R. M. and Shapiro, L. G. Appendix A. In Computer and Robot Vision. Addilson-Wesley, Reading, Ma, 1992.
[15]
Jain, A., Ross, A. and Prabhakar, S. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14, 1 (2004), 4--20.
[16]
Kaburlasos, V. G., Athanasiadis, I. N., Mitkas, P. A., and Petridis, V. Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation. International Journal of Approximate Reasoning, 45 (2007), 152--188.
[17]
Kanhangad, V., Kumar, A., and Zhang, D. A unified framework for contactless hand verification. IEEE Transactions on Information Forensics and Security, 6, 3 (September 2011).
[18]
Kumar, A. and Zhang, D. Personal recognition using hand shape and texture. IEEE Transactions on Image Processing, 15, 8 (2006), 2454--2461.
[19]
Landwehr, N., Hall, M. and Frank, E. Logistic Model Trees. Machine Learning, 59, (1-2) (2005), 161--205.
[20]
Luque-Baena, R. M., Elizondo, D., López-Rubio, E., Palomo, E. J., and Watson, T. Assessment of geometric features for individual identification and verification in biometric hand systems. Expert Systems with Applications, 40, 9 (July 2013), 3580--3594.
[21]
Maltoni, D., Maio, D., Jain, A. K. and Prabhakar, S. Handbook of Fingerprint Recognition. Springer, London, 2000.
[22]
Morales, A., Ferrer, M., Alonso, J. and Travieso, C. Comparing infrared and visible illumination for contactless hand based biometric scheme. International Carnahan conference on security technology, ICCST (2008), 191--197.
[23]
Otsu, N. A. Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 1 (1979), 62--66.
[24]
Platt, J. C. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In Advances in kernel methods. MIT Press Cambridge, MA, USA, 1999.
[25]
Rodriguez, J. J., Kuncheva, L. I. and Alonso, C. J. Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 10 (2006), 1619--1630.
[26]
Sanchez-Reillo, R., Sanchez-Avila, C., and Gonzalez-Marcos, A. Biometric identification through hand geometry measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 10 (Oct 2000), 1168--1171.
[27]
Witten, I. H., Eibe, F., and Hall, M. A. Data mining. Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, 2011.

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  • (2022)Security and Usability of a Personalized User Authentication Paradigm: Insights from a Longitudinal Study with Three Healthcare OrganizationsACM Transactions on Computing for Healthcare10.1145/35646104:1(1-40)Online publication date: 12-Oct-2022

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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 April 2015

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Author Tags

  1. biometric
  2. classification
  3. hand geometry
  4. personal recognition

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2022)Security and Usability of a Personalized User Authentication Paradigm: Insights from a Longitudinal Study with Three Healthcare OrganizationsACM Transactions on Computing for Healthcare10.1145/35646104:1(1-40)Online publication date: 12-Oct-2022

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