access icon free Long range correlation of preceded pixels relations and application to off-line signature verification

Lately, off-line signature verification systems have been reintroduced based on the idea of modelling the signature images with various relations among their pixels. In this paper, a modified version of the partially ordered set feature extraction procedure is presented by enabling distant range interactions between preceded relations of pixel groups. In this way, the spatial diversity of correlation that exists among signature features can be exploited. The motive behind this approach is related to our belief that the particular idiosyncratic writing style characteristics of each individual will be present over the whole length of the signature. Experiments involve the well-known Center of Excellence for Document Analysis and Recognition (CEDAR) and Ministerio de Ciencia y Tecnologia (MCYT) datasets in two popular writer dependent training modes: In the first mode, genuine and simulated forgeries were utilized, while in the second one genuine and random forgeries only were utilized during the training stage of the classifier. In both cases, the testing phase is exploited with genuine, simulated and random forgeries while receiver operating characteristic along with decision oriented FAR, FRR and average error metrics are assessing the proposed feature extraction method. The results obtained, show that the long range correlation of grid features can be efficiently employed for off-line signature verification.

Inspec keywords: handwriting recognition; feature extraction; digital signatures

Other keywords: false rejection ratio; offline signature verification; false acceptance ratio; long range correlation; pixel groups; FRR; FAR; preceded pixels relations; distant range interactions; receiver operating characteristic; signature images; testing phase; average error; feature extraction method; feature extraction procedure; spatial diversity; idiosyncratic writing style characteristics; signature features

Subjects: Image recognition; Computer vision and image processing techniques; Data security

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