Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/72052
| Título: | Writer identification based on graphology techniques | Autores/as: | Santana, Omar Travieso, Carlos M. Alonso, Jesús B. Ferrer, Miguel A. |
Clasificación UNESCO: | 3307 Tecnología electrónica | Palabras clave: | Artificial Neural Networks (Nns) Biometric Identification Graphology Techniques Support Vector Machines (Svms) Writer Identification |
Fecha de publicación: | 2008 | Publicación seriada: | Proceedings - International Carnahan Conference on Security Technology | Conferencia: | 42nd Annual 2008 IEEE International Carnahan Conference on Security Technology, ICCST 2008 | Resumen: | In this work an innovative system biometric of writers' identification based in technical expert calligraphic and graphology on the handwritten script is presented. It has been developed working in off-line mode on a Spanish words image database, formed by 29 different individuals. All the extractions of characteristics carried out on the images and that they have been used for the identification, they were carried out by means of the estimate of several elements object using studies of The French Graphology School, and that they are commonly employed by the handwriting experts in judicial matters. The success percentage achieved with 5 of these characteristics from this database of 29 writers is of 99.34%. In new experimentations, with these same parameters and enlarging the database to 70 users, a rate of success of 92% was reached. ©2008 IEEE. | URI: | http://hdl.handle.net/10553/72052 | ISBN: | 9781424418176 | ISSN: | 1071-6572 | DOI: | 10.1109/CCST.2008.4751297 | Fuente: | Proceedings - International Carnahan Conference on Security Technology [ISSN 1071-6572], p. 167-173, (Diciembre 2008) |
| Colección: | Actas de congresos |
Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.