Writer verification using texture-based features

In this work, we propose a writer verification system that takes into account texture-based features and dissimilarity representation. Textures of the handwritings are created based on the inherent properties of the writer. Independent of the writing style, the proposed method reduces the spaces between lines, words, and characters, producing a texture that keeps the main features thus avoiding the complexity of segmentation. We also address an important issue of verification system, i.e., the number of writers used for training. Our experiments show that the number of writers do not have an important impact on the overall error rate, but it has an important role in reducing the false acceptance of the verification system. We show that the false acceptance decreases as the number of writers increases. Finally, the ROC curves produced by different classifiers trained with different texture descriptors are combined using the maximum likelihood analysis, producing a ROC combined classifier. A set of experiments on a database composed of 315 writers show the efficiency of the texture-based features and the ROC combination scheme. Experimental results report an overall error rate of about 4%. This performance compares to the state of the art. Besides, the combination scheme is able to considerably reduce the false-positive rates while maintaining the same true-positive rates.