To improve the security and stability of biometric handwriting samples a Bio-Hash algorithm for handwriting was introduced in [1]. It utilizes features to describe how the sample was written, but the current set of features does not characterize the visual appearance of the sample itself. In this paper we present a set of new features derived from handwriting forensics and OCR algorithms to address this issue. Furthermore, here the security of the old and new sets of features is evaluated for

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... eir resilience against a new, fully automated attack trying to compute raw data matching a given hash vector. The main contributions of this paper are: The introduction of new features with a potential to increase the attack resilience of the Bio-Hash algorithm, and, an improvement of the attack approach from [6] to produce more realistic looking synthetic handwriting signals.