Sharif Digital Repository

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... 

The low-dimensional i-vector representation of speech segments is used in the state-of-the-art text-independent speaker verification systems. However, i-vectors were deemed unsuitable for the text-dependent task, where simpler and older speaker recognition approaches were found more effective. In this work, we propose a straightforward hidden Markov model (HMM) based extension of the i-vector approach, which allows i-vectors to be successfully applied to text-dependent speaker verification. In our approach, the Universal Background Model (UBM) for training phrase-independent i-vector extractor is based on a set of monophone HMMs instead of the standard Gaussian Mixture Model (GMM). To...