A new online signature verification system based on combining Mellin transform, MFCC and neural network

Abstract

In this work a new online signature verification system based on Mellin transform in combination with an MFCC is presented. In the first step we extract signals x(t) and y(t) from each signature and then the novel pre-processing algorithm by Mellin transform is performed. The key property of Mellin transform is the scale invariance which makes the features insensitive to different signature scale. The feature is extracted by Mel Frequency Cepstral Coefficient (MFCC). Subsequently, feature extraction is used to extract coefficient for each signature to construct a feature vector. These vectors are then fed into two classifiers: Neural network with multi-layer perception architecture and linear classifier used in conjunction with PCA and then results are compared. In order to evaluate the effectiveness of the system several experiments are carried out. Online signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the combination proposed method with neural network have better performance. The result shows that the proposed algorithm achieved 97% accuracy rate and higher speed rate in comparison with other methods.