Orthogonal function representation for online signature verification: which features should be looked at?

• Published in: IET biometrics Vol. 2; no. 4; pp. 137 - 150
• Main Authors: Parodi, Marianela, Gómez, Juan C, Liwicki, Marcus, Alewijnse, Linda
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.12.2013; Institution of Engineering and Technology; John Wiley & Sons, Inc
• Subjects: Applied sciences; approximation theory; behavioural sciences; Biometrics; Chinese signature styles; Competition; Complement; Cryptography; database management systems; Datasets; Detection, estimation, filtering, equalization, prediction; digital forensics; Digital signatures; discriminative power; Exact sciences and technology; FHE; forensic handwriting experts; Forgery; Handwriting; handwriting recognition; Handwritten signature verification; Information, signal and communications theory; Legendre polynomials; online signature verification; orthogonal function representation; Orthogonal functions; orthogonal series expansions; Pattern recognition; Polynomials; Series expansion; Signal and communications theory; Signal processing; Signal, noise; Signature analysis; Signatures; signing process; Special Issue: Selected papers from International Conference on Frontiers in Handwriting Recognition (ICFHR 2012); Telecommunications and information theory; time function analysis; Time functions; verification error rates; Wavelet analysis; wavelet decomposition; Wavelet transforms; Western signature styles; writer behaviour; digital forensics; approximation theory; FHE; wavelet transforms; behavioural sciences; orthogonal function representation; discriminative power; Western signature styles; wavelet decomposition; database management systems; handwriting recognition; writer behaviour; verification error rates; signature analysis; Chinese signature styles; online signature verification; forensic handwriting experts; orthogonal series expansions; time function analysis; signing process; Legendre polynomials; Discriminant analysis; State of the art; Error rate; Forensic aspect; Pattern recognition; Legendre polynomial; Handwriting recognition; Signing; Wavelet transformation; Orthogonal function; Authentication; On line processing; Database; Series expansion; Signal processing; Time curve; Chinese; Feasibility; Handwritten character recognition; Signature analysis; Manuscript character
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2013.0025

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.