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.
['Marianela Parodi', 'Juan Carlos Gómez', 'Abdel Belaïd']
A Circular Grid-Based Rotation Invariant Feature Extraction Approach for Off-line Signature Verification