Advertisement

Quality Measures for Online Handwritten Signatures

  • Nesma Houmani
  • Sonia Garcia-Salicetti
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 292)

Abstract

This chapter tackles the problem of quality of online signature samples. Several works in the literature point out signature complexity and signature stability as main quality criteria for this behavioral biometric modality. The drawback of these works is the measurement of such criteria separately. In this study, we propose to analyze such criteria with a different unifying view, in terms of entropy-based measures. We consider signature complexity as the intrinsic disorder of a signature instance and variability as the intra-class disorder of a set of genuine signatures. We introduce a novel statistical measure of complexity for signature samples and analyze it relatively to Personal Entropy that we proposed in former works. We study the power of both measures for an automatic writer categorization on several databases. We show that such categories retrieve separately on one hand degraded data and on the other hand good quality signatures. Finally, the degradation of signatures due to mobile acquisition conditions is quantified by our entropy-based measures.

Keywords

Signature complexity Signature variability Entropy Hierarchical clustering Hidden Markov Models Writer categories Online signature verification 

References

  1. 1.
    Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Trans Syst Man Cybern Part C Appl Rev 38(5):609–635Google Scholar
  2. 2.
    Garcia-Salicetti S, Houmani N, Ly-Van B, Dorizzi B, Alonso Fernandez F, Fierrez J, Ortega-Garcia J, Vielhauer C, Scheidat T (2009) On-line Handwritten signature verification. In: Petrovska-Delacrétaz D, Chollet G, Dorizzi B (eds) Guide to biometric reference systems and performance evaluation, Springer, London, pp 125–164Google Scholar
  3. 3.
    Garcia-Salicetti S, Houmani N (2009) Digitizing tablet. In: Li Stan Z (ed) Encyclopedia of biometrics, Springer, XXXII, pp 224–228, ISBN: 978-0-387-73004-2Google Scholar
  4. 4.
    Sabourin R, Plamondon R, Lorette G (1992) Off-line identification with handwritten signature images: survey and perspectives. In: Baird HS, Bunke H, Yamamoto K (eds) Structured document image analysis, Springer, Berlin, pp 219–234Google Scholar
  5. 5.
    Sabourin R (1997) Off-Line signature verification: recent advances and perspectives. In: Proceedings of Brazilian symposium on document image analysis, Curitiba, Brazil. In: Murshed NA, Bortolozzi F (eds) Advances in document image analysis, vol 1339. Springer, Berlin, LNCS, pp 84–98Google Scholar
  6. 6.
    Brault J, Plamondon R (1989) How to detect problematic signers for automatic signature verification. In: Proceedings of the International Canadian Conference on Security Technology (ICCST), Zurich, Switzerland, pp 127–132Google Scholar
  7. 7.
    Boulétreau V, Vincent N, Sabourin R, Emptoz H (1998) Handwriting and signature: one or two personality identifiers?. In Proceedings of the 14th international conference on pattern recognition, vol 2. Laos Alamitos, pp 1758–1760Google Scholar
  8. 8.
    Boulétreau V (1997) Towards a handwriting classification by fractal methods, PhD thesis. Institut National des Sciences Appliquées de Lyon, FranceGoogle Scholar
  9. 9.
    Alonso-Fernandez F, Fairhurst MC, Fierrez J, Ortega-Garcia J (2007) Impact of signature legibility and signature type in off-line signature verification. In: IEEE Biometrics symposium BSYM, Baltimore, USA, pp 1–6Google Scholar
  10. 10.
    Alonso-Fernandez, F (2008) Biometric sample quality and its application to multimodal authentication systems, PhD thesis, Universidad Politecnica de MadridGoogle Scholar
  11. 11.
    Di Lecce V, Di Mauro G, Guerriero A, Impedovo S, Pirlo G, Salzo A, Sarcinella L (1999) Selection of reference signatures for automatic signature verification. In: Proceedings of the international conference on document analysis and recognition (ICDAR’99), Bangalore, India, pp 597–600Google Scholar
  12. 12.
    Alonso-Fernandez F, Fairhurst MC, Fierrez J, Ortega-Garcia J (2007) Automatic measures for predicting performance in off-line signature. IEEE Proceedings o the international conference on image processing, ICIP, vol 1. San Antonio, USA, pp 369–372Google Scholar
  13. 13.
    Cover TM, Thomas JA (2006) Elements of information theory. 2nd edn, Wiley, New YorkGoogle Scholar
  14. 14.
    Garcia-Salicetti S, Houmani N, Dorizzi B (2009) A novel criterion for writer enrolment based on a time- normalized signature sample entropy measure. EURASIP J Adv Sig Process 2009:964746. doi: 10.1155/2009/964746
  15. 15.
    Garcia-Salicetti S, Houmani N, Dorizzi B (2008) A client-entropy measure for on-line signatures. In: IEEE Biometrics Symposium (BSYM), Tampa, USA, pp 83–88Google Scholar
  16. 16.
    Houmani N, Garcia-Salicetti S, Dorizzi B (2008) A novel personal entropy measure confronted with online signature verification systems’ performance. In: Proceedings of IEEE 2nd international conference on biometrics: theory, applications and systems (BTAS’2008), Washington, USAGoogle Scholar
  17. 17.
    Houmani N, Garcia-Salicetti S, Dorizzi B (2009) On assessing the robustness of pen coordinates, pen pressure and pen inclination to short-term and long-term time variability with personal entropy. In: Proceedings of IEEE 3rd international conference on biometrics: theory, applications and systems (BTAS ’09), Washington, USA, pp 1–6Google Scholar
  18. 18.
    Rabiner L, Juang BH (1993) Fundamentals of speech recognition, Signal processing series. Prentice Hall, Englewood CliffsGoogle Scholar
  19. 19.
    Jain AK, Murty MN, Flynn PJ (1999) Data clustering data: a review. ACM Comput Surv 31(3):264–323CrossRefGoogle Scholar
  20. 20.
    Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Addison Wesley, BostonGoogle Scholar
  21. 21.
    Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. Intell Inform Syst J 17(2–3):107–145CrossRefMATHGoogle Scholar
  22. 22.
    Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, Faundez-Zanuy M, Espinosa V, Satue A, Hernaez I, Igarza J-J, Vivaracho C, Escudero D, Moro Q-I (2003) MCYT baseline corpus: a bimodal biometric database. IEE Proc Vis Image Sig Process Spec Issue Biometrics Internet 150(6):395–401CrossRefGoogle Scholar
  23. 23.
    Hubert L, Schultz J (1976) Quadratic assignment as a general data-analysis strategy. British J Math Stat Psychol 29:190–241CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    Dudoit S, Fridlyand J (2002) A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 3(7):Research0036.1–0036.21Google Scholar
  25. 25.
    Galbally J, Fierrez J, Ortega-Garcia J (2007) Classification of handwritten signatures based on name legibility, in defense and security symposium. In: proceedings of SPIE, biometric technologies for human identification, 6539, Orlando, USAGoogle Scholar
  26. 26.
    Galbally J, Fierrez J, Freire MR, Ortega-Garcia J (2007) Feature selection based on genetic algorithms for on-line signature verification. In: Proceedings of IEEE workshop on automatic identification advanced technologies, Alghero, Italy, pp 198–203Google Scholar
  27. 27.
    Chakravarthy VS, Kompella B (2003) The shape of handwritten characters. Pattern Recogn Lett 24(12):1901–1913CrossRefGoogle Scholar
  28. 28.
    Yeung D, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) SVC2004: First international signature verification competition. In: Proceedings of the international conference on biometric authentication, Hong Kong, China, LNCS 3072, Springer, pp 16–22Google Scholar
  29. 29.
    Houmani N, Mayoue A, Garcia-Salicetti S, Dorizzi B, Khalil MI, Moustafa MN, Abbas H, Muramatsu D, Yanikoglu B, Kholmatov A, Martinez-Diaz M, Fierrez J, Ortega-Garcia J, Roure Alcobé J, Fabregas J, Faundez-Zanuy M, Pascual-Gaspar JM, Cardeñoso-Payo V, Vivaracho-Pascual C (2011) BioSecure signature evaluation campaign (BSEC’2009): evaluating online signature algorithms depending on the quality of signatures. Pattern Recogn 45(3):993–1003Google Scholar
  30. 30.
    Houmani N, Garcia-Salicetti S, Dorizzi B, Montalvão J, Coutinho Canuto J, Vasconcelos Andrade M, Qiao Y, Wang X, Scheidat T, Makrushin A, Muramatsu D, Putz-Leszczynska J, Kudelski M, Faundez-Zanuy M, Pascual-Gaspar J, Cardeñoso-Payo V, Vivaracho-Pascual C, Argones Rúa E, Luis Alba Castro J, Kholmatov A, Yanikoglu B (2011) BioSecure signature evaluation campaign (ESRA’2011): evaluating systems on quality-based categories of skilled forgeries. In: Proceedings of the international joint conference on biometrics, WashingtonGoogle Scholar
  31. 31.
  32. 32.
    Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro JL, Gonzalez-Agulla E, Otero-Muras E, Garcia-Salicetti S, Allano L, Ly-Van B, Dorizzi B, Kittler J, Bourlai T, Poh N, Deravi F, Ng MNR, Fairhurst M, Hennebert J, Humm A, Tistarelli M, Brodo L, Richiardi J, Drygajlo A, Ganster H, Sukno FM, Pavani SK, Frangi A, Akarun L, Savran A (2010) The multiscenario multienvironment biosecure multimodal database (BMDB). IEEE Trans Pattern Anal Mach Intell 32(6): 1097–1111Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Laboratoire SIGMAESPCI ParisTechParisFrance
  2. 2.CNRS UMR 5157 SAMOVAR, CEA Saclay Nano-InnovInstitut Mines-Telecom/Télécom SudParisGif sur Yvette CedexFrance

Personalised recommendations