A Circular Grid-Based Rotation Invariant Feature Extraction Approach for Off-line Signature Verification

One of the main challenges in off-line signature verification systems is to make them robust against rotation of the signatures. A new technique for rotation invariant feature extraction based on a circular grid is proposed in this paper. Graphometric features for the circular grid are defined by adapting similar features available for rectangular grids, and the property of rotation invariance of the Discrete Fourier Transform (DFT) is used in order to achieve robustness against rotation. A Support Vector Machine (SVM) based classifier scheme is used for classification tasks. Experimental results on a public database show that the proposed verification system has a performance comparable to similar state-of-the-art signature verification systems with the additional advantage of being robust against rotation of the signatures.

[1]  Miguel A. Ferrer,et al.  Off-line Signature Verification Based on High Pressure Polar Distribution , 2008 .

[2]  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).

[3]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[4]  Enrique Frías-Martínez,et al.  Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition , 2006, Eng. Appl. Artif. Intell..

[5]  Flávio Bortolozzi,et al.  An Off-Line Signature Verification System Using HMM and Graphometric Features , 2001 .

[6]  Yuan Yan Tang,et al.  Offline signature verification:A new rotation invariant approach , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[7]  M. Elif Karsligil,et al.  Off-line signature verification and recognition by Support Vector Machine , 2005, 2005 13th European Signal Processing Conference.

[8]  Jesús Francisco Vargas-Bonilla,et al.  Off-line Handwritten Signature GPDS-960 Corpus , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Flávio Bortolozzi,et al.  An off-line signature verification method based on the questioned document expert's approach and a neural network classifier , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[12]  Flávio Bortolozzi,et al.  A comparison of SVM and HMM classifiers in the off-line signature verification , 2005, Pattern Recognit. Lett..

[13]  Cinthia Obladen de Almendra Freitas,et al.  The graphology applied to signature verification , 2005 .

[14]  Flávio Bortolozzi,et al.  Off-line signature verification using HMM for random, simple and skilled forgeries , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[15]  Miguel Angel Ferrer-Ballester,et al.  Offline geometric parameters for automatic signature verification using fixed-point arithmetic , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.