In this paper we present a framework for real-time online signature verification scenarios. The proposed framework is based on state-of-the-art feature extraction and Gaussian Mixture Model (GMM) classification. While our signature verification library is generally applicable to any input device using digital pens, we have implemented verification scenarios using the Anoto digital pen. As such our automated signature verification framework becomes an interesting commodity for industry, because the Anoto SDK is easy to apply and the GMM-based classification can be seamlessly integrated. The novelty of this work is the application of our framework that takes real-time online signature verification to every scenario where digital pens may potentially be used. In this paper we describe several scenarios where our framework has been applied, including signatures in financial contracts or ordering processes. We also propose a general approach to integrate the GMM-descriptions into electronic ID-cards in order to also store behavioral biometrics on these cards. In experiments we have measured the performance of the signature verification system when skilled forgeries were present. The interest shown by our partner financial institutions and the results of our initial evaluations indicate that our signature verification framework suits exactly the demands of our clients.
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