Nonlinear Dynamics Tools for Offline Signature Verification Using One-class Gaussian Process

Abstract

One of the major problems in biometrics and in document forensics is the offline mode of signature verification. This study aims to present a novel approach of verifying an individual's signature through offline images of handwriting. The approach proposed here relies on a global method which considers signature images as waveforms. First, image decompositions are in terms of a series of wavelet sub-bands at some specific levels. Wavelet sub-bands are then extended so as to obtain waveforms. Each waveform is quantized by two Nonlinear Dynamics Tools in order to generate feature vectors. Multi-Resolution Box-Counting (MRBC) fractal dimension algorithm as well as probabilistic finite state automata (PFSA) are applied separately to signature waveforms. In the training and verification phase, we propose the one-class Gaussian process (GP) priors based on writer-independent approach. As one of the main parameters, optimal decision threshold is selected from False Accept Rate (FAR) and False Reject Rate (FRR) curves. The presented system was tested on two Persian databases (PHBC and UTSig) as well as on two Latin databases (MCYT-75 and CEDAR). In fact, the results produced by this method were generally better in terms of the four signature databases than the state-of-the-art results.