It is often difficult to obtain sufficient signature samples to train up a signature verification system. With insufficient training samples, the estimation of the statistical parameters such as the mean feature vector and covariance matrix becomes unreliable and system performance would be degraded. In this paper, a two-dimensional elastic matching method to generate additional samples is proposed to expand the limited training set so that a better estimate of the statistical variations can be obtained. The method differs from existing ones in that it is more suitable for the generation of signature samples. Besides this, a set of peripheral features, which is useful in describing both the internal and external structures of signatures, is employed to represent the signatures in the verification process. Results showed that verification system with the additional samples could reduce the error rate from 15.6% to 11.4%. It also outperformed another existing method which estimates the class covariance matrix through optimization techniques. Results also demonstrated that the peripheral features are useful for signature verification.