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

• Autorzy: Adamski M. , Saeed K.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

offline signature verification (SV); shape contexts

PL

weryfikacja podpisu w trybie offline

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2013
• Tom: Vol. 22
• Strony: 195--201
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Adamski M.

• Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland.

autor

Saeed K.

• Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Cracow, Poland

Bibliografia

• [1] ADAMSKI M., SAEED K., Offline Signature Identification and Verification using Noniterative Shape Context Algorithm, Journal of Medical Informatics and Technologies, 2009, Vol. 13, pp. 47-58.
• [2] ADAMSKI M., SAEED K., TABĘDZKI M., RYBNIK M., Signature System Based on Extended Shape Context Descriptors, International Conference on Biometrics and Kansei Engineering (ICBAKE), 2013, p. 267-272.
• [3] BELONGIE S., MALIK J., PUZICHA J., Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, Vol. 24, pp. 509-522.
• [4] DOROZ R., PORWIK P., PARA T., WROBEL K., Dynamic signature recognition based on velocity changes of some features, International Journal of Biometrics, 2008, Vol. 1, No. 1, pp. 47-62.
• [5] IMPEDOVO D., PIRLO G. , Automatic Signature Verification: The State of the Art, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 2008, Vol. 38, pp. 609-635.
• [6] JAIN A. K., ROSS A., PRABHAKAR S. , An Introduction to Biometric Recognition, IEEE Transactions on Circuits and Systems for Video Technology, 2004, Vol. 14, pp. 4-20.
• [7] PIRLO G., IMPEDOVO D., On the measurement of local stability of handwriting: An application to static signature verification, in IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), 2010, pp. 41-44.
• [8] SAEED K., TABĘDZKI M., RYBNIK M., ADAMSKI M., K3M - A Universal Algorithm for Image Skeletonization and a Review of Thinning Techniques, International Journal of Applied Mathematics and Computer Science, 2010, Vol. 20, pp. 317-335.
• [9] VARGAS J. F., FERRER M. A., TRAVIESO C. M., ALONSO J. B., Off-line Handwritten Signature GPDS-960 Corpus, in Ninth International Conference on Document Analysis and Recognition (ICDAR), 2007, pp. 764-768.