On-line signature verification using Gaussian Mixture Models and small-sample learning strategies

Keywords: On-line signature verification, Gaussian Mixture Models, Universal Background Model, Variational GMMSupervector, Bayesian learning


This  paper  addresses  the  problem  of  training  on-line  signature  verification   systems when the number of training samples is small, facing the real-world scenario when  the  number  of  available  signatures  per  user  is  limited.  The  paper  evaluates  nine  different   classification  strategies  based  on  Gaussian  Mixture  Models  (GMM),  and  the  Universal   Background  Model  (UBM)  strategy,  which  are  designed  to  work  under  small-sample  size   conditions. The GMM’s learning strategies include the conventional Expectation-Maximisation  algorithm and also a Bayesian approach based on variational learning. The signatures are  characterised  mainly  in  terms  of  velocities  and  accelerations  of  the  users’  handwriting   patterns.  The  results  show  that  for  a  genuine  vs.  impostor  test,  the  GMM-UBM  method  is   able to keep the accuracy above 93%, even when only 20% of samples are used for training (5  signatures). Moreover, the combination of a full Bayesian UBM and a Support Vector Machine  (SVM)  (known  as  GMM-Supervector)  is  able  to  achieve  99%  of  accuracy  when  the  training   samples exceed 20. On the other hand, when simulating a real environment where there are  not available impostor signatures, once again the combination of a full Bayesian UBM and a  SVM, achieve more than 77% of accuracy and a false acceptance rate lower than 3%, using  only 20% of the samples for training.

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Author Biographies

Julián David Arias Londoño, Universidad de Antioquia
Department of Electronic Engineering and Telecommunications
Gabriel Jaime Zapata Zapata, Universidad de Antioquia
Professor, Department of Systems Engineering
Jesús Francisco Vargas Bonilla, Universidad de Antioquia
Department of Electronic Engineering and Telecommunications
Juan Rafael Orozco Arroyave, Friedrich-Alexander University Erlangen-Nürnberg

Pattern Recognition Lab


S. Liu and M. Silverman, “A practical guide to biometric security technology,” IT Professional, vol. 3, pp. 27–32, Jan. 2001.

K. Franke and J. Ruiz-del Solar, “Soft-biometrics: Soft-computing technologies for biometric-applications,” in Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing, AFSS ’02, (London, UK, UK), pp. 171–177, Springer-Verlag, 2002.

S. Impedovo and G. Pirlo, “Verification of handwritten signatures: An overview,” in Proceedings of the 14th International Conference on Image Analysis and Processing, ICIAP ’07, (Washington, DC, USA), pp. 191–196, IEEE Computer Society, 2007.

J. F. Vargas, M. A. Ferrer, C. M. Travieso, and J. B. Alonso, “Off-line signature verification based on grey level information using texture features,” Pattern Recognition, vol. 44, pp. 375–385, Feb. 2011.

M. D. Malekar and S. Patel, “Off-line signature verification using artificial neural network,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 9, pp. 127–130, 2013.

K. Manoj, “Signature verification using neural network,” International Journal on Computer Science and Engineering, vol. 4, no. 9, pp. 1498–1504, 2012.

E. Argones-Ra and J. L. Alba-Castro, “Online signature verification based on generative models.,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 42, no. 4, pp. 1231–1242, 2012.

D. Reynolds, T. Quatieri, and R. Dunn, “Speaker verification using adapted Gaussian Mixture Models,” Digital Signal Processing, vol. 10, no. 1-3, pp. 19–41, 2000.

L. Wan and B. Wan, “On-line signature verification with two-stage statistical models,” in Proceedings of the 2005 Eight International Conference on Document Analysis and Recognitio, ICDAR’05, 2005.

M. Martinez-Diaz, J. Fierrez, and J. Ortega-Garcia, “Universal background models for dynamic signature verification,” in Proceedings of the First IEEE International Conference on Biometrics: Theory, Applications, and Systems, BTAS’07, 2007.

W. Campbell, D. Sturim, and D. Reynolds, “Support vector machines using GMM supervectors for speaker verification,” IEEE Signal Processing Letters, vol. 13, no. 5, pp. 308 – 311, 2006.

H. Attias, “Inferring parameters and structure of latent variable models by variational Bayes,” in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, pp. 21–30, 1999.

S. Garca, J. Luengo, and F. Herrera, Data Preprocessing in Data Mining. New York, NY, USA: Springer, 2015.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

D. Reynolds, “Speaker identification and verification using Gaussian mixture speaker models,” Speech Communication, vol. 17, no. 1-2, pp. 91–108, 1995.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. New Jersey, NY, USA: Wiley-Interscience, 2nd ed., 2000.

M. Ferras, L. Cheung-Chi, C. Barras, and J. Gauvain, “Comparison of speaker adaptation methods as feature extraction for SVM-based speaker recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 6, pp. 1366 – 1378, 2010.

C. Leggetter and P. Woodland, “Maximum likelihood liner regression for speaker adaptation of continuous density hidden Markov models,” Computer Speech and Language, vol. 9, no. 2, pp. 171–185, 1995.

C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

K. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press, 2012.

N. Nasios and A. Bors, “Variational learning for Gaussian Mixture Models,” IEEE Trans. Systems, Man, Cybern., Part B,, vol. 36, no. 4, pp. 849–862, 2006.

V. Sahu, H. Mishra, and C. Shekar, “Variational bayes adapted GMM based for audio clip classification models,” in Proc. Int. Conf. Pattern Recognition Mach. Intell., pp. 513–518, 2009.

J. Fierrez-Aguilar, J. Ortega-Garcia, D. Torre-Toledano, and J. Gonzalez-Rodr ́ıguez, “Biosec baseline corpus: A multimodal biometric database,” Pattern Recognition, pp. 1389–1392, 2007.

J. Montalvao, N. Houmani, and B. Dorizzi, “Comparing GMM and parzen in automatic signature recognition a step backward or forward,” in Proceedings of CBA2010, pp. 4463–4468, 2010.

N. Sae-Bae and N. D. Memon, “Online signature verification on mobile devices,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 6, pp. 933–947, 2014.

J. Fierrez-Aguilar, L. Nanni, J. L.-P. nalba, J. Ortega-Garcia, and D. Maltoni, “An on-line signature verification system based on fusion of local and global information,” in Proceedings of the International Conference on Audio- and Video-based Biometric Person Authentication (T. Kanade, A. Jain, and N. K. Ratha, eds.), vol. 3546 of Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag.

S. Garcia-Salicetti, N. Houmani, B. Ly-Van, B. Dorizzi, F. Alonso-Fernandez, J. Fierrez, J. Ortega-Garcia, C. Vielhauer, and T. Scheidat, Online Handwritten Signature Verification. Springer-Verlag, London, 2008. ISBN 978-1-84800-291-3.

How to Cite
Arias Londoño J. D., Zapata Zapata G. J., Vargas Bonilla J. F., & Orozco Arroyave J. R. (2016). On-line signature verification using Gaussian Mixture Models and small-sample learning strategies. Revista Facultad De Ingeniería Universidad De Antioquia, (79), 84-97. https://doi.org/10.17533/udea.redin.n79a09