Evaluation of signature verification reliability based on artificial neural networks, Bayesian multivariate functional and quadratic forms
Ivanov A.I., Lozhnikov P.S., Sulavko A.E.


Penza Scientific and Research Electrotechnical Institute, Penza, Russia,
Omsk State Technical University, Omsk, Russia

Full text of article: Russian language.


An experimental comparison of various functional neural networks for signature verification is performed. A signature database for the realization of the computing experiment is built. It is confirmed that up to a certain point, the increase of the decision rule dimension reduces the probability of signature verification error, with an increase in the number of neurons in the network reducing the number of errors. A higher-dimension multi-dimensional Bayes functional with stronger inter-feature correlation is found to perform better. The best result for the signature verification is obtained using networks of Bayesian multidimensional functional, with false acceptance rate of FRR = 0.0288 and false rejection rate of FAR = 0.0232.

neural networks, network of quadratic forms, multi-dimensional Bayes functional, signature reproduction peculiarities, biometric features.

Ivanov AI, Lozhnikov PS, Sulavko AE. Evaluation of signature verification reliability based on artificial neural networks, Bayesian multivariate functional and quadratic forms. Computer Optics 2017; 41(5): 765-774. DOI: 10.18287/2412-6179-2017-41-5-765-774.


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