caa a22 4500 606160191 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1779-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1779-6 Multilevel fusion for fast online signature recognition using multi-section VQ and time modelling [Elektronische Daten] [Muhammad Razzak, Bandar Alhaqbani] Signature recognition is one of the most important biometrics authentication methods, is an integral part of current business activities, and is considered a non-invasive and non-threatening process. This paper presents an online signature verification system using multi-section VQ. We have used multi-section codebooks for signature recognition by splitting the signature into several sections with every section having its own codebook. The final result is based on the score level fusion of the results of each codebook. Moreover, multilevel fusion is performed in this trial to improve the accuracy. We have used SVC database that contains skilled forgery samples. Our experimental results on SVC database have shown 100% accuracy with 0.003 EER. The Natural Computing Applications Forum, 2014 Biometrics recognition nationallicence Signature recognition nationallicence Vector quantization nationallicence Signature normalisation nationallicence Razzak Muhammad College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia aut Alhaqbani Bandar College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1117-1127 0941-0643 26:5<1117 2015 26 521 https://doi.org/10.1007/s00521-014-1779-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1779-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Razzak Muhammad College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia aut NATIONALLICENCE 700 1- Alhaqbani Bandar College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1117-1127 0941-0643 26:5<1117 2015 26 521