access icon free Online signature verification using i-vector representation

© The Institution of Engineering and Technology
Received 09/04/2017, Accepted 28/11/2017, Revised 21/11/2017, Published 20/12/2017

Signature verification (SV) is one of the common methods for identity verification in banking, where for security reasons, it is very important to have an accurate method for automatic SV (ASV). ASV is usually addressed by comparing the test signature with the enrolment signature(s) signed by the individual whose identity is claimed in two manners: online and offline. In this study, a new method based on the i-vector is proposed for online SV. In the proposed method, a fixed-length vector, called i-vector, is extracted from each signature and then this vector is used for template making. Several techniques such as nuisance attribute projection (NAP) and within-class covariance normalisation (WCCN) are also investigated in order to reduce the intra-class variation in the i-vector space. In the scoring and decision making stage, they also propose to apply a 2-class support vector machine method. Experimental results show the proposed method could achieve 8.75% equal error rate (EER) on SigWiComp2013 database in the best case. On SVC2004 database, it also achieved 5% EER that means 11% relative improvement compared with the best reported result. In addition to its considerable accuracy gain, it has shown significant improvement in the computational cost over conventional dynamic time warping method.

Inspec keywords: pattern classification; support vector machines; digital signatures

Other keywords: equal error rate; online signature verification; low-dimensional i-vector representation; SVC2004 database; SV; 2-class support vector machine method; ASV; supervised binary classification task; cosine similarity; within-class covariance normalisation; SigWiComp2013 database; nuisance attribute projection; enrolment signature; fixed-length vector

Subjects: Knowledge engineering techniques; Data security

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