Signature Verification for Offline Skilled Forgeries Using Textural Features

This study explores the effectiveness of two textural measurements on signature verification for skilled forgeries. These texture features include 2D autoregressive coefficients and run-length distributions. Signature images corresponding to 521 writers from the GPDS960 database were used to evaluate the performance of these features. Comparison of the proposed textural features with a number of state-of-the-art features realized interesting results. The run-length features out perform other features for a sufficient number of genuine signatures in the training dataset.