A Multiple Algorithm Approach to Textural Features Extraction in Offline Signature Recognition

Autor:	Noah Oluwatobi Akande, Tinuke Omolewa Oladele, Jide Kehinde Adeniyi, Roseline Oluwaseun Ogundokun, Tunde Taiwo Adeniyi
Rok vydání:	2020
Předmět:	High rate Local binary patterns Computer science Biometric trait ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Grey level Signature database Algorithm Signature (logic) Signature recognition Image (mathematics)
Zdroj:	Information Systems ISBN: 9783030633950 EMCIS
DOI:	10.1007/978-3-030-63396-7_36
Popis:	Signature is a biometric trait that has piqued the interest of researchers. This is due to its high rate of acceptability. Offline signature in particular, has been around for a while and hence its suitability as a biometric trait. This paper proposes an offline signature recognition system using a multiple algorithm approach. The system accepts handwritten signature, filters the signature and crops the signature region. The Local Binary Pattern (LBP) of the signature image is then obtained. After this, Grey Level Co-occurrence Matrix (GLCM) is applied. Statistical features are then extracted. The difference in the stored features and the extracted features was obtained. The output is compared with a threshold for discrimination. This research aims at improving the performance of offline signature recognition using its textural features. The designed system gave an FRR and FAR of 8.6%, 4.6% respectively for MYCT signature database and 8.8%, 5.2% for GPDS signature database.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e4631ad3ff5cc3909e31ea2ba9072641 https://doi.org/10.1007/978-3-030-63396-7_36 Zobrazit plný text záznamu