Online Signature Verification Based on Legendre Series Representation: Robustness Assessment of Different Feature Combinations

In this paper, orthogonal polynomials series are used to approximate the time functions associated to the signatures. The coefficients in these series expansions, computed resorting to least squares estimation techniques, are then used as features to model the signatures. Different combinations of several time functions (pen coordinates, incremental variation of pen coordinates and pen pressure), related to the signing process, are analyzed in this paper for two different signature styles, namely, Western signatures and Chinese signatures of a publicly available Signature Database. Two state-of-the-art classification methods, namely, Support Vector Machines and Random Forests are used in the verification experiments. The proposed online signature verification system delivers error rates comparable to results reported over the same signature datasets in a previous signature verification competition.