A Human Factors Study of Graphical Passwords Using Biometrics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


One mode of authentication used in modern computing systems is graphical passwords. Graphical passwords are becoming more popular because touch-sensitive and pen-sensitive technologies are becoming ubiquitous. In this paper, we construct the “BioSketch” database, which is a general database of sketch-based passwords (SkPWs) with pressure information used as a biometric property. The BioSketch database is created so that recognition approaches may be commensurable with the benchmark performances. Using this database, we are also able to study the human-computer interaction (HCI) process for SkPWs. In this paper, we compare a generalized SKS recognition algorithm with the Fréchet distance in terms of the intra/inter-class variations and performances. The results show that the SKS-based approach achieves as much as a 7 % and 17 % reduction in equal error rate (EER) for random and skilled forgeries respectively.


Dynamic Time Warping Equal Error Rate False Acceptance Rate False Rejection Rate Benchmark Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The information in this paper is based on work partially funded by the United States Army Research Office (ARO) grant W911NF-04-D-0003-0019.


  1. 1.
    Agarwal, P.K., Avraham, H.K., Kaplan, H., Sharir, M.: Computing the discrete freéchet distance in subquadratic time. In: Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 156–167 (2013)Google Scholar
  2. 2.
    Alt, H., Godau, H.: Computing the fréchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 5(1–2), 75–91 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)zbMATHGoogle Scholar
  4. 4.
    Biddle, R., Chiasson, S., Van Oorschot, P.C.: Graphical passwords: learning from the first twelve years. ACM Comput. Surv. 44(4), 1–25 (2012)CrossRefGoogle Scholar
  5. 5.
    De Angeli, A., Coventry, L., Johnson, G., Renaud, K.: Is a picture really worth a thousand words? Exploring the feasibility of graphical authentication systems. Int. J. Hum. Comput. Stud. 63(1–2), 128–152 (2005)CrossRefGoogle Scholar
  6. 6.
    Dunphy, P., Yan, J.: Do background images improve “draw a secret” graphical passwords? In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 36–47 (2007)Google Scholar
  7. 7.
    Gao, H., Guo, X., Chen, X., Wang, L., Liu, X.: Yet another graphical password strategy. In: Proceedings of the Annual Computer Security Applications Conference, pp. 121–129 (2008)Google Scholar
  8. 8.
    Goldberg, J., Hagman, J.: Doodling our way to better authentication. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 868–869 (2002)Google Scholar
  9. 9.
    Jermyn, I., Mayer, A., Monrose, F., Reiter, M., Rubin, A.: The design and analysis of graphical passwords. In: Proceedings of the 8th USENIX Security Symposium, pp. 1–14 (1999)Google Scholar
  10. 10.
    Krish, K., Heinrich, S., Snyder, W.E., Cakir, H., Khorram, S.: Global registration of overlapping images using accumulative image features. Pattern Recogn. Lett. 31, 112–118 (2010)CrossRefGoogle Scholar
  11. 11.
    Martinez-Diaz, M., Fierrez, J., Galbally, J.: The DooDB graphical password database: data analysis and benchmark results. IEEE Access 1, 596–605 (2013)CrossRefGoogle Scholar
  12. 12.
    Martinez-Diaz, M., Fierrez, J., Martin-Diaz, C., Ortega-Garcia, J.: DooDB: a graphical password database containing doodles and pseudo-signatures. In: 12th International Conference on Frontiers in Handwriting Recognition, pp. 339–344 (2010)Google Scholar
  13. 13.
    Riggan, B.S.: Recognition of sketch-based passwords with biometric information using a generalized simple K-space model. Ph.D thesis/Dissertation, North Carolina State University (2014)Google Scholar
  14. 14.
    Rousson, M., Cremers, D.: Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 757–764. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Snyder, W.E.: A strategy for shape recognition. In: Srivastava, A. (ed.) Workshop on Challenges and Opportunities in Image Understanding, College Park, MD, January 2007Google Scholar
  16. 16.
    Tao, H., Adams, C.: Pass-go: a proposal to improve the usability of graphical passwords. Int. J. Netw. Secur. 7(2), 273–292 (2008)Google Scholar
  17. 17.
    Varenhorst, C.: Passdoodles: a lightweight authentication method. MIT Research Science Institute, July 2004Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.US Army Research OfficeDurhamUSA

Personalised recommendations