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Recently, several papers have proposed pseudo dynamic methods for automatic handwritten signature verification. Each of these papers uses texture measures of the gray level signature strokes. This paper explores the usefulness of local binary pattern (LBP) and local directional pattern (LDP) texture measures to discriminate off-line signatures. A comparison between several texture normalizations is made so as to look for reducing pen dependence. The experiments conducted with MCYT off-line and GPDS960Graysignature corpuses show that LDPs are more useful than LBPs for automatic verification of static signatures. Additionally, the results show that the LDP codes of the contour are more discriminating than the LDPs of the stroke interior, although their combination at score level improves the overall scheme performance. The results are obtained by modeling the signatures with a Support Vector Machine (SVM) trained with genuine samples and random forgeries, while random and simulated forgeries have been used for testing it.