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





on-line signature verification, Gaussian Mixture Models, Universal Background Model, variational GMM-supervector, 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, University of Antioquia

Department of Electronic Engineering and Telecommunications.

Gabriel Jaime Zapata-Zapata, University of Antioquia

Professor, Department of Systems Engineering.

Jesús Francisco Vargas-Bonilla, University of Antioquia

Department of Electronic Engineering and Telecommunications.

Juan Rafael Orozco-Arroyave, Friedrich-Alexander University Erlangen-Nürnberg

Pattern Recognition Lab.


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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

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