Neural Network and kNN Classifiers for On-Line Signature Verification

In this paper a method for on-line signature verification is presented. The proposed approach consists of the following consecutive steps: feature selection and classification. Experiments are carried out on SUsig database [5] of genuine and forgery signatures of 89 users. The results obtained by applying two different types of classifiers (NN and k-nearest neighbours) are compared. For each user, several NN and kNN models are evaluated by 10-fold cross validation and LOOCV respectively. The “optimal” models are found together with their parameters: number of hidden neurons for NN, type of signature forgeries for training, input features and value of k. The influence of the signature forgery type (random and skilled) over the feature selection and verification is investigated as well.

[1]  Giuseppe Pirlo,et al.  Automatic Signature Verification: The State of the Art , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Réjean Plamondon,et al.  Automatic Signature Verification: The State of the Art - 1989-1993 , 1994, Int. J. Pattern Recognit. Artif. Intell..

[3]  Georgi Gluhchev,et al.  Signature Verification via "Hand-Pen" Motion Investigation* , 2006 .

[4]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[5]  Georgi Gluhchev,et al.  A New Approach to Signature-Based Authentication , 2007, ICB.

[6]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[7]  R. R. Hocking,et al.  Computational Efficieucy in the Selection of Regression Variables , 1970 .

[8]  R. R. Hocking,et al.  Selection of the Best Subset in Regression Analysis , 1967 .

[9]  Berrin A. Yanikoglu,et al.  SUSIG: an on-line signature database, associated protocols and benchmark results , 2008, Pattern Analysis and Applications.

[10]  Jonas Richiardi,et al.  Local and global feature selection for on-line signature verification , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[11]  Berrin A. Yanikoglu,et al.  Online Signature Verification Using Fourier Descriptors , 2009, EURASIP J. Adv. Signal Process..

[12]  Jarrod Trevathan,et al.  Neural Network-based Hwritten Signature Verification , 2008, J. Comput..

[13]  Berrin A. Yanikoglu,et al.  Identity authentication using improved online signature verification method , 2005, Pattern Recognit. Lett..

[14]  V. S. Nalwa.  Automatic on-line signature verification , 1997 .