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1

Signature verification: A comprehensive study of the hidden signature method

Putz-Leszczyńska J.

International Journal of Applied Mathematics and Computer Science

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2015

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Vol. 25, no. 3

659--674

EN

Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature—an artificial signature which is created by minimizing the mean misalignment between itself and the signatures from the enrollment set. We present a few hidden signature estimation methods together with their comprehensive comparison. The hidden signature opens a number of new possibilities for signature analysis. We apply statistical properties of the hidden signature to normalize the error signal of the verified signature and to use the misalignment on the normalized errors as a verification basis. A result, we achieve satisfying error rates that allow creating an on-line system, ready for operating in a real-world environment.

2

Offline signature identification and verification using noniterative shape context algorithm

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

47--52

EN

The paper presents experimental results on offline signature identification and verification. At the first stage of the presented system, the binary image of the signature undergoes skeletonization process using KMM algorithm to have a thinned, one pixel-wide line, to which a further reduction is applied. For each thinned signature image a fixed number of points comprising the skeleton line are selected. The recognition process is based on comparing the reference signatures with the questioned samples using distance measure computed by means of Shape Context algorithm. The experiments were carried out using a database containing signatures of 20 individuals. For the verification process random forgeries were used to asses the system error. The main advantage of the presented approach lies in utilizing only one reference signature for both identification and verification tasks, whereas the achieved results are comparable with respect to the systems that use several training samples per subject.

3

A statistical approach for off-line signature verification (SV)

Das M.T., Dulger L.C., Dulger H.E.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

33--39

EN

This paper includes off line Signature Verification (SV) process with test results using the proposed algorithm Particle Swarm Optimization-Neural Network (PSO-NN) together with statistical analysis, Chi-square test. The verification process is performed in four steps. Signature images are scanned (data acquisition) and image processing is applied to make images suitable for extracting features (pre-processing). Each pre-processed image is then used to extract relevant geometric parameters (feature extraction) that can distinguish signatures of different volunteers. Finally, the proposed verification algorithm is tested on the database that includes 1350 skilled and genuine signatures taken from 25 volunteers. The Chi-square test is applied to see how the signature data fits with probability test function.

4

Podpis elektroniczny

Stokłosa J.

Logistyka

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2002

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R. 32, nr 1

51-51

5

Identyfikacja biometryczna - blaski i cienie

Swarcewicz R.

Logistyka

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2002

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R. 32, nr 4

44-47