Comparative Analysis of Neural Network and Dynamic Time Warping in Online Signature Verification

In this paper we explore the possibilities of using Neural Networks with transfer learning approach and Dynamic Time Warping for online signature verification. Verification process of proposed methods is based on SVC-2004 signature database, which contains genuine and skilled forgery signatures. Experimental results show that approach using Convolutional Neural Networks is more successful and this method is achieving an Equal Error Rate (EER) as low as 2.25% using 10-fold cross-validation. These results are competitive with other published works.