Links Between Markov Models and Multilayer Perceptrons
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
On-Line Signature Verification by Dynamic Time-Warping
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
On-line Signature Verification Using Local Shape Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Identity authentication using improved online signature verification method
Pattern Recognition Letters
A fast Mellin and scale transform
EURASIP Journal on Applied Signal Processing
Online Signature Classification and its Verification System
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Aligning and segmenting signatures at their crucial points through DTW
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
IEEE Transactions on Signal Processing
Target dependent score normalization techniques and their application to signature verification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On-line signature verification using LPC cepstrum and neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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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.