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3 Journal of Medical Informatics & Technologies

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Offline signature verification based on shape contexts using shared and user-specific thresholds

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

2013

Vol. 22

195--201

In this paper we present a system for offline signature verification based on Shape Context Descriptors. The system input are binarized images of handwritten signatures from GPDS database available for non-commercial research. During preprocessing each signature image is thinned using KMM algorithm in order to obtain 1-pixel wide skeleton. The feature vector is built from Shape Context Descriptors computed for selected points on skeletonized signature line. The verification process is based on the distance measure that uses Shape Context Descriptors. The presented system is evaluated using random and skilled forgeries with shared and user-specific thresholds.

Offline signature identification and verification using noniterative shape context algorithm

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

2009

Vol. 13

47--52

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.

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

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

Journal of Medical Informatics & Technologies

2009

Vol. 13

33--39

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.