Biometric Hash based on Statistical Features of Online Signatures

This paper presents a new approach to generate biometric hash values based on statistical features in online signature signals. Whilst the output of typical online signature verification systems are threshold-based true-false decisions, based on a comparison between test sample signals and sets of reference signals, our system responds to a signature input with a biometric hash vector, which is calculated based on an individual interval matrix. Especially for applications, which require key management strategies (e.g. e-Commerce, smart cards), hash values are of great interest, as keys can be derived directly from the hash value, whereas a verification decision can only grant or refuse access to a stored key. Further, our new approach does not require storage of templates for reference signatures, thus increases the security of the system. In our prototype implementation, the generated biometric hash values are calculated on a pen-based PDA and used for key generation for a future secure data communication between a PDA and a server by encryption. First tests show that the system is actually able to generate stable biometric hash values of the users and although the system was exposed to skilled forgeries, no test person was able to reproduce another subject’s hash vector. During tests, we were able to tune the system to a FAR of 0% at a FRR level of 7.05%.