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Multilevel fusion for fast online signature recognition using multi-section VQ and time modelling

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Abstract

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.

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Authors and Affiliations

  1. College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

    Muhammad Imran Razzak & Bandar Alhaqbani

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  1. Muhammad Imran Razzak
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  2. Bandar Alhaqbani
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Correspondence to Muhammad Imran Razzak.

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Razzak, M.I., Alhaqbani, B. Multilevel fusion for fast online signature recognition using multi-section VQ and time modelling. Neural Comput & Applic 26, 1117–1127 (2015). https://doi.org/10.1007/s00521-014-1779-6

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  • DOI: https://doi.org/10.1007/s00521-014-1779-6

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