Advertisement

Annales Des Télécommunications

, Volume 62, Issue 1–2, pp 36–61 | Cite as

Biosecure reference systems for on-line signature verification: A study of complementarity

  • Sonia Garcia-Salicetti
  • Julian Fierrez-Aguilar
  • Fernando Alonso-Fernandez
  • Claus Vielhauer
  • Richard Guest
  • Lorene Allano
  • Tung Doan Trung
  • Tobias Scheidat
  • Bao Van Ly
  • Jana Dittmann
  • Bernadette Dorizzi
  • Javier Ortega-Garcia
  • Joaquin Gonzalez-Rodriguez
  • Martino Bacile Di Castiglione
  • Michael Fairhurst
Multimodal Biometrics
  • 91 Downloads

Abstract

In this paper, we present an integrated research study in On-line Signature Verification undertaken by several teams that participate in the BioSecure Network of Excellence. This integrated work started during the First BioSecure Residential Workshop, has as main objective the development of an On-line Signature Verification evaluation platform. As a first step, four On-line Signature Verification Systems based on different approaches are evaluated and compared following the same experimental protocol on MCYT signature database, which is the largest existing on-line western signature database publicly available with 16500 signatures from 330 clients. A particular focus of work documented in this paper is multi-algorithmic fusion in order to study the complementarity of the approaches involved. To this end, a simple fusion method based on the Mean Rule is used after a normalization phase.

Key words

Biometrics Handwriting Signature On-line processing Authentication Comparative study Statistical method Hidden Markov model Reference model Mathematical distance Experimental study Research program 

Systèmes de référence de biosecure pour la vérification de signature en ligne: Une étude de la complémentarité

Résumé

Dans cet article, nous présentons un travail commun sur la vérification de signature enligne, réalisé par 4 équipes qui participent au Réseau d’Excellence BioSecure. Ce travail commun, débuté durant le premier « Workshop » résidentiel, a pour principal objectif le développement d’une plateforme d’évaluation pour la vérification de la signature en-ligne. Tout d’abord, quatre systèmes de vérification de signature en-ligne basés sur différentes approaches sont évalués et comparés en utilisant le même protocole expérimental sur la base de signatures MCYT, la plus grande base existante de signatures en-ligne disponible, avec 16500 signatures de 330 personnes. Ensuite, l’accent est mis sur la fusion multi-algorithmique afin d’étudier la complémentarité des approches impliquées. Pour cela, une méthode de fusion simple est utilisée, basée sur une moyenne des scores après une phase de normalisation.

Mots clés

Biométrie écriture manuscrite Signature Traitement en ligne Authentification étude comparative Méthode statistique Modèle Markov caché Modèle référence Distance mathématique étude expérimentale Programme recherche 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Rabiner L., Juang B.H., Fundamentals of Speech Recognition, Prentice Hall Signal Processing Series, 1993.Google Scholar
  2. [2]
    Levenshtein V.I., Binary codes capable of correcting deletions, insertions and reversals, Soviet Physics 10, pp. 707–710, 1966.MathSciNetGoogle Scholar
  3. [3]
    Ortega-Garcia J., Fierrez-Aguilar J., Simon D., Gonzalez J., Faundez-Zanuy M., Espinosa V., Satue A., Hernaez I., Igarza J., Vivaracho C., Escudero C., Moro Q., MCYT baseline corpus: a bimodal biometric database, IEE Proc. Vision, Image and Signal Processing 150, no 6, pp. 391–401, 2003.Google Scholar
  4. [4]
    Ross A., Jain A.K., Information Fusion in Biometrics, Pattern Recognition Letters 24, pp. 2115–2125, 2003.Google Scholar
  5. [5]
    Jain A.K., Ross A., Multibiometric Systems, Communications of the acm 47, no 1, Jan. 2004.Google Scholar
  6. [6]
    Kittler J., Hatef M., Duin R.P.W., Matas J., On Combining Classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no 3, pp. 226–239, March 1998.Google Scholar
  7. [7]
    Indovina M., Uludag U., Snelick R., Mink A., Jain A., Multimodal Biometric Authentication Methods: A cots Approach, Proc. MMUA 2003, pp. 99–106, Santa Barbara, California, USA, Dec. 2003.Google Scholar
  8. [8]
    Jain A., Nandakumar K., Ross A., Score Normalization in Multimodal Biometrie Systems, Pattern Recognition 38, no 12, pp. 2270–2285, Dec. 2005.Google Scholar
  9. [9]
    Haykin S., Neural networks: a comprehensive foundation, Upper Saddle River, NJ, 1999.Google Scholar
  10. [10]
    Ly Van B., Garcia-Salicetti S., Dorizzi B., Fusion of hmm’s Likelihood and Viterbi Path for On-line Signature Verification, Proc. BioAW 2004, Lecture Notes in Computer Science 3087, Prague, Czech Republic, pp. 318–331, May 2004.Google Scholar
  11. [11]
    Fierrez-Aguilar J., Ortega-Garcia J., Gonzalez-Rodriguez J., Target dependent score normalization techniques and their application to signature verification, IEEE Trans, on Systems, Man and Cybernetics, part C 35, pp. 418–425, 2005.Google Scholar
  12. [12]
    Yeung D., Chang H., Xiong Y., George S., Kashi R., Matsumoto T., Rigoll G., svc2004: First International Signature Verification Competition, Proc. of ICBA, Lecture Notes in Computer Science 3072, pp. 16–22, 2004.Google Scholar
  13. [13]
    Kholmatov A., Yanikoglu B.A., Identity authentication using improved online signature verification method, Pattern Recognition Letters, 26, no15, pp. 2400–2408, 2005.Google Scholar
  14. [14]
    Schimke S., Vielhauer C., Dittmann J., Using Adapted Levenshtein Distance for On-Line Signature Authentication, Proceedings of the ICPR 2004, IEEE 17th International Conference on Pattern Recognition, ISBN 0-7695-2128-2, 2004.Google Scholar
  15. [15]
    Garcia-Salicetti S., Beumier C., Chollet G., Dorizzi B., Leroux-Les Jardins J., Lunter J., Ni Y., Petrovska-Delacretaz D., Biomet: a Multimodal Person Authentication Database Including Face, Voice, Fingerprint, Hand and Signature Modalities, Proc. AVBPA 2003, Guildford, uk, pp. 845–853, July 2003.Google Scholar
  16. [16]
    Martin A., Doddington G., Kamm T., Ordowski M., Przybocki M., The det Curve in Assessment of Detection Task Performance, Proc. eurospeech’97, 4, pp. 1895–1898, Rhodes Greece, 1997.Google Scholar
  17. [17]
    Ortega-Garcia J., Fierrez-Aguilar J., Martin-Rello J. & Gonzalez-Rodriguez J., Complete Signal Modeling and Score Normalization for Function-Based Dynamic Signature Verification, Proc. of AVBPA’03, Lecture Notes in Computer Science 2688, pp. 658–667, 2003.Google Scholar
  18. [18]
    Maio D., Maltoni D., Cappelli R., Wayman J.L., Jain A.K., FVC2000: Fingerprint Verification Competition, IEEE Trans, on Pattern Anal, and Machine Intell. 24, no3, pp. 402–412, 2002.Google Scholar
  19. [19]
    Sakamoto D., Morita H., Ohishi T., Komiya Y., Matsumoto T., On-line Signature Verification Algorithm Incorporating Pen Position, Pen Pressure and Pen Inclination Trajectories, Proc. of the IEEE Intl. Conf on Acoustics, Speech, and Signal Processing, ICASSP, pp. 993–996, 2001.Google Scholar

Copyright information

© Institut Telecom / Springer-Verlag France 2007

Authors and Affiliations

  • Sonia Garcia-Salicetti
    • 1
  • Julian Fierrez-Aguilar
    • 2
  • Fernando Alonso-Fernandez
    • 2
  • Claus Vielhauer
    • 3
  • Richard Guest
    • 4
  • Lorene Allano
    • 1
  • Tung Doan Trung
    • 1
  • Tobias Scheidat
    • 3
  • Bao Van Ly
    • 1
  • Jana Dittmann
    • 3
  • Bernadette Dorizzi
    • 1
  • Javier Ortega-Garcia
    • 2
  • Joaquin Gonzalez-Rodriguez
    • 2
  • Martino Bacile Di Castiglione
    • 4
  • Michael Fairhurst
    • 4
  1. 1.Dept. EPHGET/INT (Institut National des Télécommunications)EvryFrance
  2. 2.ATVS/Biometrics Research Lab., Escuela Politecnica SuperiorUniversidad Autonoma de MadridSpain
  3. 3.School of Computer Science, Dept. ITIOtto-Von-Guericke University of MagdeburgMagdeburgGermany
  4. 4.Department of ElectronicsUniversity of KentCanterburyUK

Personalised recommendations