Neural network and kNN classifiers for on-line signature verification

Abstract

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.