Skip to main content
SpringerLink
Log in
Book cover

Multimedia Processing, Communication and Computing Applications pp 229–240Cite as

User Dependent Features in Online Signature Verification

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 213))

Abstract

In this paper, we propose a novel approach for verification of on-line signatures based on user dependent feature selection and symbolic representation. Unlike other signature verification methods, which work with same features for all users, the proposed approach introduces the concept of user dependent features. It exploits the typicality of each and every user to select different features for different users. Initially all possible features are extracted for all users and a method of feature selection is employed for selecting user dependent features. The selected features are clustered using Fuzzy C means algorithm. In order to preserve the intra-class variation within each user, we recommend to represent each cluster in the form of an interval valued symbolic feature vector. A method of signature verification based on the proposed cluster based symbolic representation is also presented. Extensive experimentations are conducted on MCYT-100 User (DB1) and MCYT-330 User (DB2) online signature data sets to demonstrate the effectiveness of the proposed novel approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Jain AK, Griess FD, Connel SD (2002) On-line signature verification. Pattern Recogn 35:2963–2972

    Article  MATH  Google Scholar 

  2. Nanni L, Lumini A (2006) Advanced methods for two-class problem formulation for on-line signature verification. Neurocomputing 69:854–857

    Article  Google Scholar 

  3. Ismail MA, Gad S (2002) Offline Arabic signature verification. Pattern Recogn 33:1727–1740

    Article  Google Scholar 

  4. Plamondon R, Lorette G (1989) Automatic signature verification and writer identification: the state of the art. Pattern Recogn 2(2):63–94

    Google Scholar 

  5. Lee LL, Berger T, Aviczer E (1996) Reliable on-line signature verification systems. IEEE Trans Pattern Anal Mach Intell 18:643–649

    Article  Google Scholar 

  6. Kashi R, Hu J, Nelson WL, Turin W (1998) A hidden Markov model approach to on-line handwritten signature verification. IJDAR 1:102–109

    Article  Google Scholar 

  7. Zhang K, Prathikakis I, Cornelis J, Nyssen E (2003) Using landmarks to establish point-to-point correspondence between signatures. Pattern Anal Appl 3:69–73

    Article  Google Scholar 

  8. Fierrez AJ, Krawczyk S, Ortega-Garcia J, Jain AK (2005) Fusion of local and regional approaches for on-line signature verification. International workshop on biometric recognition system (IWBRS), LNCS, vol 3781. pp 188–196

    Google Scholar 

  9. Fierrez AJ, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn Lett 28(16):2325–2334

    Article  Google Scholar 

  10. Kholmatov A, Yanikoglu B (2005) Identity authentication using improved online signature verification method. Pattern Recogn Lett 26:2400–2408

    Article  Google Scholar 

  11. Baltzakis H, Papamarkos N (2001) A new signature verification technique based on a two stage neural classifier. Eng Appl Artif Intell 14:95–103

    Article  Google Scholar 

  12. Guru DS, Prakash HN (2009) Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans Pattern Anal Mach Intell 31(6):1059–1073

    Article  Google Scholar 

  13. Guru DS, Prakash HN, Manjunath S (2009) Online signature verification: an approach based on cluster representation of global features. In: Seventh international conference on advances in pattern recognition, pp 209–212

    Google Scholar 

  14. Cai D, Zhang C, He X (2010) unsupervised feature selection for multi-cluster data. In: 16th ACM SIGKDD conference on knowledge discovery and data mining (KDD’10), pp 333–342

    Google Scholar 

  15. Bezdez JC (1981) Pattern recognition with fuzzy objective algorithms. Plenum, New York

    Book  Google Scholar 

  16. Garcia OJ, Fierrez AJ, Simon D (2003) MCYT baseline corpus: a bimodal database. In: IEEE proceedings on vision, image and signal processing, vol 150. pp 395–401

    Google Scholar 

  17. Fierrez AJ, Nanni L, Penalba JL, Garcia JO, Maltoni D (2005) An on-line signature verification system based on fusion of local and global information. AVBPA, LNCS 3546:523–532

    Google Scholar 

  18. Nanni L (2006) Experimental comparison of one-class classifier for on-line signature verification. Neurocomputing 69:869–873

    Google Scholar 

Download references

Acknowledgments

We thank Dr. Julian Fierrez Aguillar, Biometric Research Lab-AVTS, Spain for providing MCYT Online signature dataset. We also thank Deng Cai, Associate Professor, Zhejiang University, China for sharing his work on unsupervised feature selection for multi-cluster data.

Author information

Authors and Affiliations

  1. Department of Studies in Computer Science, Manasagangothri, University of Mysore, Mysore, 570006, Karnataka, India

    D. S. Guru & K. S. Manjunatha

  2. Postgraduate Department of Studies in Computer Science, JSS College of Arts, Commerce and Science, Mysore, 570025, Karnataka, India

    S. Manjunath

Authors
  1. D. S. Guru
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. S. Manjunatha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Manjunath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. S. Guru .

Editor information

Editors and Affiliations

  1. Master of Computer Applications, PES Institute of Technology, Banashankari 3rd stage, Near Hoskerehalli Cross 100 Feet, Bangalore, 560085, Karnataka, India

    Punitha P. Swamy

  2. Studies in Computer Science, University of Mysore, Manasagangotri, Mysore, 570006, Karnataka, India

    Devanur S. Guru

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer India

About this paper

Cite this paper

Guru, D.S., Manjunatha, K.S., Manjunath, S. (2013). User Dependent Features in Online Signature Verification. In: Swamy, P., Guru, D. (eds) Multimedia Processing, Communication and Computing Applications. Lecture Notes in Electrical Engineering, vol 213. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1143-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1143-3_19

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1142-6

  • Online ISBN: 978-81-322-1143-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation