Biometric Identification Using Gaze and Mouse Dynamics During Game Playing

  • Paweł KasprowskiEmail author
  • Katarzyna Harezlak
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


The paper presents a method, developed for identifying people based on their mouse and gaze dynamics obtained between two mouse clicks. The data used to evaluate the method was collected when participants were playing a simple shooting game. Various statistics were calculated taking mouse and gaze speed and acceleration into account. 24 participants took part in the experiment conducted to check if the proposed method may be applied for identification and authentication purposes. Although, the obtained averaged results (EER 11% and F1-score 90%) showed that statistics calculated for a combination of recorded mouse and gaze positions may be successfully used for authenticating people, it must be noticed that there were significant differences in performance among participants. For about half of them the results were satisfactory, with the best EER 4% and F1-score 99%, while for the worst participant EER equal to 23% and F1-score to 76% were obtained. These results suggest that finding one set of features that is suitable for every person may be a challenging task. It may imply that for behavioral biometric building separate sets of features for each enrolled person should be considered.


Biometric identification Mouse Gaze 



This work was supported by Statutory Research funds of Institute of Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK-213/RAU2/2018).


  1. 1.
    Ahmed, A.A.E., Traore, I.: A new biometric technology based on mouse dynamics. IEEE Trans. Dependable Secure Comput. 4(3), 165–179 (2007)CrossRefGoogle Scholar
  2. 2.
    Asha, S., Chellappan, C.: Authentication of e-learners using multimodal biometric technology. In: International Symposium on Biometrics and Security Technologies 2008, ISBAST 2008, pp. 1–6. IEEE (2008)Google Scholar
  3. 3.
    Bailey, K.O., Okolica, J.S., Peterson, G.L.: User identification and authentication using multi-modal behavioral biometrics. Comput. Secur. 43, 77–89 (2014)CrossRefGoogle Scholar
  4. 4.
    Biedert, R., Frank, M., Martinovic, I., Song, D.: Stimuli for gaze based intrusion detection. In: Park, J., Leung, V., Wang, C.L., Shon, T. (eds.) Future Information Technology, Application, and Service. LNEE, vol. 164, pp. 757–763. Springer, Netherlands (2012). Scholar
  5. 5.
    Calix, K., Connors, M., Levy, D., Manzar, H., MCabe, G., Westcott, S.: Stylometry for e-mail author identification and authentication. In: Proceedings of CSIS Research Day, Pace University (2008)Google Scholar
  6. 6.
    Connaughton, R., Bowyer, K.W., Flynn, P.J.: Fusion of face and iris biometrics. In: Burge, M., Bowyer, K. (eds.) Handbook of Iris Recognition. ACVPR, pp. 219–237. Springer, London (2013). Scholar
  7. 7.
    Conti, V., Militello, C., Sorbello, F., Vitabile, S.: A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(4), 384–395 (2010)CrossRefGoogle Scholar
  8. 8.
    Darwish, A., Pasquier, M.: Biometric identification using the dynamic features of the eyes. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6. IEEE (2013)Google Scholar
  9. 9.
    Deravi, F., Guness, S.P.: Gaze trajectory as a biometric modality. In: BIOSIGNALS, pp. 335–341 (2011)Google Scholar
  10. 10.
    Gamboa, H., Fred, A.: A behavioral biometric system based on human-computer interaction. In: Defense and Security, pp. 381–392. International Society for Optics and Photonics (2004)Google Scholar
  11. 11.
    Harezlak, K., Rzeszutek, J., Kasprowski, P.: The eye tracking methods in user interfaces assessment. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. SIST, vol. 39, pp. 325–335. Springer, Cham (2015). Scholar
  12. 12.
    Hashiaa, S., Pollettb, C., Stampc, M., Hall, M.: On using mouse movements as a biometric (2005)Google Scholar
  13. 13.
    Holland, C., Komogortsev, O.V.: Biometric identification via eye movement scanpaths in reading. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2011)Google Scholar
  14. 14.
    Jorgensen, Z., Yu, T.: On mouse dynamics as a behavioral biometric for authentication. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, pp. 476–482. ACM (2011)Google Scholar
  15. 15.
    Kasprowski, P.: Mining of eye movement data to discover people intentions. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 355–363. Springer, Cham (2014). Scholar
  16. 16.
    Kasprowski, P., Harezlak, K.: Fusion of eye movement and mouse dynamics for reliable behavioral biometrics. Pattern Anal. Appl. 21(1), 91–103 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kasprowski, P., Komogortsev, O.V., Karpov, A.: First eye movement verification and identification competition at BTAS 2012. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 195–202. IEEE (2012)Google Scholar
  18. 18.
    Kasprowski, P., Ober, J.: Eye movements in biometrics. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 248–258. Springer, Heidelberg (2004). Scholar
  19. 19.
    Kinnunen, T., Sedlak, F., Bednarik, R.: Towards task-independent person authentication using eye movement signals. In: Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications, pp. 187–190. ACM (2010)Google Scholar
  20. 20.
    Komogortsev, O.V., Jayarathna, S., Aragon, C.R., Mahmoud, M.: Biometric identification via an oculomotor plant mathematical model. In: Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications, pp. 57–60. ACM (2010)Google Scholar
  21. 21.
    Maeder, A.J., Fookes, C.B.: A visual attention approach to personal identification (2003)Google Scholar
  22. 22.
    Mehrotra, H., Rattani, A., Gupta, P.: Fusion of iris and fingerprint biometric for recognition. In: Proceedings of International Conference on Signal and Image Processing, pp. 1–6 (2006)Google Scholar
  23. 23.
    de Oliveira, P.X., et al.: Mouse movement biometric system. In: Proceedings of the CSIS Research Day (2013)Google Scholar
  24. 24.
    Pusara, M., Brodley, C.E.: User re-authentication via mouse movements. In: Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security, pp. 1–8. ACM (2004)Google Scholar
  25. 25.
    Rigas, I., Economou, G., Fotopoulos, S.: Biometric identification based on the eye movements and graph matching techniques. Pattern Recogn. Lett. 33(6), 786–792 (2012)CrossRefGoogle Scholar
  26. 26.
    Rigas, I., Komogortsev, O.V.: Biometric recognition via probabilistic spatial projection of eye movement trajectories in dynamic visual environments. IEEE Trans. Inf. Forensics Secur. 9(10), 1743–1754 (2014)CrossRefGoogle Scholar
  27. 27.
    Rose, J., Liu, Y., Awad, A.: Biometric authentication using mouse and eye movement data. J. Cyber Secur. Mob. 6(1), 1–16 (2017)CrossRefGoogle Scholar
  28. 28.
    Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115–2125 (2003)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Tan, T., Jain, A.K.: Combining face and iris biometrics for identity verification. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 805–813. Springer, Heidelberg (2003). Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland

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