In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

/pdf/automatic-signature-verification-the-state-of-the-art-68njryqnhw.pdf

Automatic Signature Verification: The State of the Art

The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters

https://www.inf.ufpr.br/lesoliveira/download/PR2010.pdf

Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers

Deep Multitask Metric Learning for Offline Signature Verification

An approach to off-line signature verification, one with an on-line flavor, is described. A sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. Performance is significantly better than that of a word-shape based signature verification method. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 95% (with 20% rejection) which is comparable to that of on-line systems.

/pdf/use-of-exterior-contours-and-shape-features-in-off-line-jjebf1etqb.pdf

Use of exterior contours and shape features in off-line signature verification

Learning strategies and classification methods for verification of signatures from scanned documents are proposed and evaluated. Learning strategies considered are writer independent- those that learn from a set of signature sample (including forgeries) prior to enrollment of a writer, and writer dependent- those that learn only from a newly enrolled individual. Classification methods considered include two distance based methods (one based on a threshold, which is the standard method of signature verification and biometrics, and the other based on a distance probability distribution), a Nave Bayes (NB) classifier based on pairs of feature bit values and a support vector machine (SVM). Two scenarios are considered for the writer dependent scenario: (i) without forgeries (one-class problem) and (ii) with forgery samples being available (two class problem). The features used to characterize a signature capture local geometry, stroke and topology information in the form of a binary vector. In the one-class scenario distance methods are superior while in the two-class SVM based method outperforms the other methods.

/pdf/learning-strategies-and-classification-methods-for-off-line-3eeaxhnqm4.pdf

M.K. Kalera

Papers

Learning strategies and classification methods for off-line signature verification