Handwritten signature verification: Online verification using a fuzzy inference system
Fuzzy inference; Personal computers; Biometric features; False acceptance rate; False rejection rate; Fuzzy inference systems; Handwritten signature verification; On-line verifications; Pressure sensitive; Signature verification; Image processing
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| _version_ | 1833351834594443264 |
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| author | Faruki M.J. Lun N.Z. Ahmed S.K. |
| author2 | 57209599618 |
| author_facet | 57209599618 Faruki M.J. Lun N.Z. Ahmed S.K. |
| author_sort | Faruki M.J. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Fuzzy inference; Personal computers; Biometric features; False acceptance rate; False rejection rate; Fuzzy inference systems; Handwritten signature verification; On-line verifications; Pressure sensitive; Signature verification; Image processing |
| format | Conference Paper |
| id | my.uniten.dspace-22840 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-228402023-05-29T14:12:37Z Handwritten signature verification: Online verification using a fuzzy inference system Faruki M.J. Lun N.Z. Ahmed S.K. 57209599618 57189523646 25926812900 Fuzzy inference; Personal computers; Biometric features; False acceptance rate; False rejection rate; Fuzzy inference systems; Handwritten signature verification; On-line verifications; Pressure sensitive; Signature verification; Image processing Biometric features posses the significant advantage of being difficult to lose, forget or duplicate. Hence, a FIS-based method is used for signature verification. FIS is well suited for this task due to the similarity between an individual signatures with subtle differences between each signature sample. Signature samples are collected using a tablet PC. The individuals draw their signatures usinga pressure sensitive pen on the tablet. Eight dynamic features are extracted from the signature data. These eight features are then fuzzified for training of a FIS. The system is then used to determine whether the signature is genuine or forged. A False Acceptance Rate (FAR) of 10.67% and a False Rejection Rate (FRR) of 8.0% demonstrate the promise of this system. � 2015 IEEE. Final 2023-05-29T06:12:37Z 2023-05-29T06:12:37Z 2016 Conference Paper 10.1109/ICSIPA.2015.7412195 2-s2.0-84971638503 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971638503&doi=10.1109%2fICSIPA.2015.7412195&partnerID=40&md5=e0bed4e69f30971f7303ee6f5827e0cb https://irepository.uniten.edu.my/handle/123456789/22840 7412195 232 237 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Faruki M.J. Lun N.Z. Ahmed S.K. Handwritten signature verification: Online verification using a fuzzy inference system |
| title | Handwritten signature verification: Online verification using a fuzzy inference system |
| title_full | Handwritten signature verification: Online verification using a fuzzy inference system |
| title_fullStr | Handwritten signature verification: Online verification using a fuzzy inference system |
| title_full_unstemmed | Handwritten signature verification: Online verification using a fuzzy inference system |
| title_short | Handwritten signature verification: Online verification using a fuzzy inference system |
| title_sort | handwritten signature verification: online verification using a fuzzy inference system |
| url_provider | http://dspace.uniten.edu.my/ |