Signature verification with chain code and geometric features

Abstract

© 2017 ACM. Signature is a kind of biometric identification that is widely used in contracts, agreements, and other legal documents. However, the signature may be imitated by deceivers. In order to verify the signature, specialists are required and they may use a lot of time to inspect the suspect signatures. This paper proposes to verify the signatures using machine learning techniques, such as neural network, decision tree, decision table, and Naïve Bayes. Feature extraction is an important part in learning process. Concept of chain code is introduced and this concept will be used for extracting some features from signature images. These features and geometric features are used to train and test in order to verify the actual signatures. The experimental results show that the proposed features can improve the accuracy of signature verification.