An Application of the 2D Gaussian Filter for Enhancing Feature Extraction in Off-line Signature Verification
Abstract-Similar to many other pattern recognition problems, feature extraction contributes significantly to the overall performance of an off-line signature verification system. To be successful, a feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. In this paper, we describe a grid-based feature extraction technique that utilises directional information extracted from the signature contour, i.e. the chain code histogram. Our experimental results for signature verification indicated that, by applying a suitable 2D Gaussian filter on the matrices containing the chain code histograms, an average error rate (AER) of 13.90% can be obtained whilst maintaining the false acceptance rate (FAR) for random forgeries as low as 0.02%. These figures are comparable or better than those reported by other state of the art feature extraction techniques such as the Modified Direction Feature (MDF) and the Gradient feature.
Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011)
Artificial Intelligence and Image Processing not elsewhere classified
Pattern Recognition and Data Mining