Assessing Textural Features for Writer Identification on Different Writing Styles and Forgeries

摘要

In this study we assess the performance of textural descriptors for writer identification on different writing styles and also on forgeries. To do that, we have performed a series of experiments using the Fire maker database, which provides for the same writer texts written on three different writing styles and also copied forged text. Our experimental protocol is based on the dissimilarity framework and SVM classifiers, which were trained with LBP (Local Binary Pattern) and LPQ (Local Phase Quantization). The 250 writers of the database were divided into different configurations to observe the impacts of different sizes of the training set on the performance of the system. Our experimental results corroborates the fact that the texture is an interesting alternative for writer identification. The classifier trained with LPQ was able to produce error rates 23 percentage points smaller than those reported in the literature for upper-case and free writing styles. Regarding the forgeries, the LPQ-based classifier goes further reducing the error rate up to 44 percentage points depending on the writing style used for training.