An individuality model for online signatures using global Fourier descriptors

The discriminative capability of a biometric is based on its individuality/uniqueness and is an important factor in choosing a biometric for a large-scale deployment. Individuality studies have been carried out rigorously for only certain biometrics, in particular fingerprint and iris, while work on establishing handwriting and signature individuality has been mainly on feature level. In this study, we present a preliminary individuality model for online signatures using the Fourier domain representation of the signature. Using the normalized Fourier coefficients as global features describing the signature, we derive a formula for the probability of coincidentally matching a given signature. Estimating model parameters from a large database and making certain simplifying assumptions, the probability of two arbitrary signatures to match in 13 of the coefficients is calculated as 4.7x10-4. When compared with the results of a verification algorithm that parallels the theoretical model, the results show that the theoretical model fits the random forgery test results fairly well. While online signatures are sometimes dismissed as not very secure, our results show that the probability of successfully guessing an online signature is very low. Combined with the fact that signature is a behavioral biometric with adjustable complexity, these results support the use of online signatures for biometric authentication.