Advertisement

Cognitive Computation

, Volume 4, Issue 2, pp 195–205 | Cite as

An Information Analysis of In-Air and On-Surface Trajectories in Online Handwriting

  • Enric Sesa-NoguerasEmail author
  • Marcos Faundez-Zanuy
  • Jiří Mekyska
Article

Abstract

This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.

Keywords

Handwriting Biometrics Information theory 

Notes

Acknowledgments

This work has been supported by FEDER and MEC, TEC2009-14123-C04-04. We also want to acknowledge the SIX (CZ.1.05/2.1.00/03.0072), CZ.1.07/2.3.00/20.0094, VG20102014033 and KONTAKT ME10123 projects for providing Jiri’s support.

References

  1. 1.
    Huber RA, Headrick AM. Handwriting identification: facts and fundamentals. Boca Raton: CRC Press; 1999.CrossRefGoogle Scholar
  2. 2.
    Forbes KE, Shanks MF, Venneri A. The evolution of dysgraphia in Alzheimer’s disease. Brain Res Bull. 2004;63:19–24.PubMedCrossRefGoogle Scholar
  3. 3.
    Neils-Strunjas J, Groves-Wright K, Mashima P, Harnish S. Dysgraphia in Alzheimer’s disease: a review for clinical and research purposes. J Speech Lang Hear Res. 2006;49:1313–30.PubMedCrossRefGoogle Scholar
  4. 4.
    Werner P, Rosenblum S, Bar-On G, Heinik J, Korczyn A. Handwriting process variables discriminating mild Alzheimer’s disease and mild cognitive impairment. J Gerontol B Psychol Sci Soc Sci. 2006;61:P228–36.PubMedCrossRefGoogle Scholar
  5. 5.
    Asıcıoglu F, Turan N. Handwriting changes under the effect of alcohol. Forensic Sci Int. 2003;132:201–10.PubMedCrossRefGoogle Scholar
  6. 6.
    Phillips JG, Ogeil RP, Müller F. Alcohol consumption and handwriting: a kinematic analysis. Hum Mov Sci. 2009;28:619–32.PubMedCrossRefGoogle Scholar
  7. 7.
    Foley RG, Lamar Miller A. The effects of marijuana and alcohol usage on handwriting. Forensic Sci Int. 1979;14:159–64.PubMedCrossRefGoogle Scholar
  8. 8.
    Tucha O, Walitza S, Mecklinger L, Stasik D, Sontag T, Lange KW. The effect of caffeine on handwriting movements in skilled writers. Hum Mov Sci. 2006;25:523–35.PubMedCrossRefGoogle Scholar
  9. 9.
    Rosenblum S, Parush S, Weiss P. The in air phenomenon: temporal and spatial correlates of the handwriting process. Percept Mot Skills. 2003;96:933.PubMedCrossRefGoogle Scholar
  10. 10.
    Viñals Carrera F, Puente Balsells ML. Grafología criminal. Herder, Barcelona, 2009.Google Scholar
  11. 11.
    Puente Balsells ML, Viñals Carrera F. Grafología y ciencia: validación con ciento cincuenta tesis doctorales, 1a en lengua caellana ed., Uoc, Barcelona, 2010.Google Scholar
  12. 12.
    Vielhauer C. Biometric user authentication for it security from fundamentals to handwriting. Boston, MA: Springer Science + Business Media Inc; 2006.Google Scholar
  13. 13.
    Plamondon R, Lorette G. Automatic signature verification and writer identification: the state of the art. Pattern Recogn. 1989;22:107–31.CrossRefGoogle Scholar
  14. 14.
    Leclerc F, Plamondon R. Automatic signature verification: the state of the art 1989–1993. Int J Pattern Recogn Artif Intell. 1994;8:643.CrossRefGoogle Scholar
  15. 15.
    Plamondon R, Srihari S. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell. 2000;22:63–84.CrossRefGoogle Scholar
  16. 16.
    Impedovo D, Pirlo G. Automatic signature verification: the state of the art. IEEE Trans Syst., Man and Cybernetics, Part C (Applications and Reviews). 2008;38:609–35.Google Scholar
  17. 17.
    Sesa-Nogueras E, Faundez-Zanuy M. Biometric recognition using online uppercase handwritten text. Pattern Recogn. 2012;45:128–44.CrossRefGoogle Scholar
  18. 18.
    Srihari S, Sung-Hyuk C, Sangjik L, Establishing handwriting individuality using pattern recognition techniques. In: Proceedings of the sixth international conference on document analysis and recognition. 2001;1195–204.Google Scholar
  19. 19.
    Zhang B, Srihari S, Analysis of handwriting individuality using word features. In Proceedings of the seventh international conference on document analysis and recognition. 2003;1142–6.Google Scholar
  20. 20.
    Hook C, Kempf J, Scharfenberg G, A novel digitizing pen for the analysis of pen pressure and inclination in handwriting biometrics, biometric authentication workshop, Prague 2004, Lecture Notes in Computer Science. 2004;3087:283–94.Google Scholar
  21. 21.
    Chapran J. Biometric writer identification: feature analysis and classification. Int J Pattern Recogn Artif Intell. 2006;20:483–503.Google Scholar
  22. 22.
    Sesa-Nogueras E, Faundez-Zanuy M. Writer recognition by means of stroke categorization based on self-organizing maps. In: Apolloni B, Bassis S, Esposito A, Morabito CF, editors. Neural nets WIRN11. Volume 234 of Frontiers in Artificial Intelligence and Applications. IOS Press; 2011. ISBN:978-1-60750-971-4. http://www.iospress.nl/book/neural-nets-wirn11/.
  23. 23.
    Shannon CE. A mathematical theory of communication. New York: American Telephone and Telegraph Co.; 1948.Google Scholar
  24. 24.
    Espinosa-Duró V, Faundez-Zanuy M, Mekyska J. Beyond cognitive signals. Cogn Comput. 2011;3:374–81.CrossRefGoogle Scholar
  25. 25.
    Espinosa-Duro V, Faundez-Zanuy M, Mekyska J, Monte-Moreno E. A criterion for analysis of different sensor combinations with an application to face biometrics. Cogn Comput. 2010;2:135–41.CrossRefGoogle Scholar
  26. 26.
    Cover TM, Thomas JA. Elements of information theory. New York: Wiley; 1991.CrossRefGoogle Scholar
  27. 27.
    MacKay DJC. Information theory, inference, and learning algorithms. Cambridge: Cambridge University Press; 2003.Google Scholar
  28. 28.
    Fierrez-Aguilar J, Galbally J, Ortega-Garcia J, Freire M, Alonso-Fernandez F, Ramos D, et al. BiosecurID: a multimodal biometric database. Pattern Anal Appl 2010;13.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Enric Sesa-Nogueras
    • 1
    Email author
  • Marcos Faundez-Zanuy
    • 1
  • Jiří Mekyska
    • 2
  1. 1.EUP MataróMataróSpain
  2. 2.Faculty of Electrical Engineering and Communication, Department of TelecommunicationsBrno University of TechnologyBrnoCzech Republic

Personalised recommendations