Online Payments Using Handwritten Signature Verification

Making payments online is inherently insecure, especially those involving credit cards where a handwritten signature is normally required to be authenticated. This paper describes a system for enhancing the security of online payments using automated handwritten signature verification. Our system combines complementary statistical models to analyse both the static features of a signature (e.g., shape, slant, size), and its dynamic features (e.g., velocity, pen-tip pressure, timing) to form a judgment about the signer's identity. This approach's novelty lies in combining output from existing Neural Network and Hidden Markov Model based signature verification systems to improve the robustness of any specific approach used alone. The system can be used to authenticate signatures for online credit card payments using an existing model for remote authentication. The system performs reasonably well and achieves an overall error rate of 2.1% in the best case.