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In this study, several feature combinations are studied to analyse their relevance for online signature verification. Different time functions associated with the signing process are analysed in order to provide some insight on their actual discriminative power. This analysis could also help forensic handwriting experts (FHEs) to further understand the signatures and the writer's behaviour. Among the different feature combinations analysed, a set of features which seems to be relevant for signature analysis by FHEs is particularly considered. The feasibility of developing a system which could complement the FHEs work is evaluated. Two different approximations of the analysed time functions are proposed, one based on the Legendre polynomials and another based on the wavelet decomposition. The coefficients in these orthogonal series expansions of the time functions are used as features to model them. Two different signature styles are considered, namely, Western and Chinese, of one of the most recent publicly available signature databases. The experimental results are promising, in particular for the features that seem to be relevant for the FHEs, since the obtained verification error rates are comparable with the ones reported in the state-of-the-art over the same datasets.

This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios ( C llr ). The system having the best Minimum Cost of Log Likelihood Ratio ( C min llr ) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.

This paper evaluates the feasibility of using only Forensic Handwriting Experts (FHEs) based features for automatic online signature verification. Both, global features and features based on the wavelet representation of the time functions associated with the signing process, which are relevant to FHEs, are considered in this paper. Two combination approaches of global and time function FHE based features are proposed. One of them, consists in a pre-classification of the signatures based on FHE global features so that gross forgeries can be discarded, followed by a Random Forest (RF) classifier using time function based FHE features. The other one, consists in a decision level fusion of two RF classifiers using global and time function FHE based features, respectively. Experimental results on a publicly available database containing Western andChinese signatures are promising in the sense that automatic online verification systems using exclusively FHE based features achieve verification performances comparable to those of the state-of-the-art over the same datasets.

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