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Dr Bikasih Thapa & Dr Maheswar Prasad (Nepal) - Hideo Wada MD PhD (japan) - Dr a Lavra Castrocatesana (Mexico) - Dr Mrs N.M. Hettiarachechui (Srilanka) - Dr Jorge Aldrete Velasco (Mexico) - Prof Hans Peter Kohler (Switzerland) - Dr Hermanus Suhartono S Sp.OG(K) PhD - Dr Isabel Pinheiro (Portugal) - Dr Suranga (Srilanka) - Jovia Dino Jansen Amsterdam,Holand - Hideo Wada MD PhD University Graduate School of Medicine Departement of Moleculer and Laboratory Medicine Japan - DR Bikash Thapa Internal Medicine Nepal University - DR Maheswar Prasad Internal Medicine Nepal University - Dr a Lavra Castro Castresana Colegio de Medicina interna de Mexico - Dr Suransa Manilgama University of Srilanka Internal Departement Medicine - Dr Mrs N.M. Hettiarachechui University of Medicine Srilanka - Dr Jorge Aldrete Velaso .Colegio de Medicina Interna de Mexico - Prof Hans Peter Kholer M.D FACD Profesor of Medicine University ot Switzerland - Dr Ramezan Ali Atace . Baqiyatallah University of Medical Sciences Departement of Micrology Tehran Iran - Ezekiel Wong Toh Yoon Dr. Gastroenterology of Japan - D Eric Beck,MD Bethesda Hospital Capitol Boelevard St Paul USA - Dr Emine Guderen Sahin Istambul University of Internal Medicine Turky - Dr Selmin Toplan Istambul University - Dr Nicholas New Australia - Dr Kughan Govinden. Tropical Infection of Internal Medicine Malaysia - Dr Godfrey M Rwegerera Princes Marina Hospital Bostwana -

Title : OFFlINE SIGNATURE FORGERY DETECTION USING HOUGH TRANSFORM

Author : DIGALA RAGHAVA RAJU, K BALAJI SUNIL CHANDRA, JANGILI RAVI KISHORE

Abstract :

In behavioural biometrics, one common method of authentication is offline signature recognition with forgery detection. An innovative offline signature forgery detection method based on Hough transform characteristics is presented in this research. One method for extracting features is the hough transform, which may identify regular forms like lines, curves, ellipses, and so on. Following the preprocessing procedures for the signature samples, we use the Hough transform to extract the shape characteristics. We do this by extracting attributes from ten real signatures and five fake signatures belonging to twenty different individuals. After that, the correlation coefficient and the thresholding approach are used to identify forgeries. A 92% accuracy rate was achieved using the suggested approach.

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Dr. Arend L Mapanawang, Sp.PD, FINASIM, PhD

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