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There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. For the set of training signature samples, two approaches are proposed. One approach measures the positional variations of the one-dimension projection profiles of the signature patterns, while the other determines the statistical variations in relative stroke positions of the two-dimensional signature patterns. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. A matrix estimation technique is also proposed to obtain a better estimation of the covariance matrix for dissimilarity computation. Results show that the proposed systems compare favorably with other methods.