Pattern Analysis and Applications

, Volume 14, Issue 1, pp 37–45 | Cite as

Efficient on-line signature recognition based on multi-section vector quantization

  • Marcos Faundez-ZanuyEmail author
  • Juan Manuel Pascual-Gaspar
Theoretical Advances


This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.


On-line signature recognition Vector quantization DTW 



This work has been supported by FEDER and MEC, TEC2006-13141-C03-02/TCM, TEC2009-14123-C04-04.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Marcos Faundez-Zanuy
    • 1
    Email author
  • Juan Manuel Pascual-Gaspar
    • 2
  1. 1.Escola Universitària Politècnica de MataróBarcelonaSpain
  2. 2.ValladolidSpain

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