Continuous Authentication on Smartphone by Means of Periocular and Virtual Keystroke

  • Silvio BarraEmail author
  • Mirko Marras
  • Gianni Fenu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11058)


Nowadays, biometric recognition and verification methods are everywhere, trying to face the security issues that constantly affect our digital-every day life. In addition, many special-purpose applications, also need a constant (continuous) verification of the user in order to avoid that a sensitive operation is executed by an impostor; as an example let think to banking operations. In this paper, a continuous authentication method on mobile device is presented, which uses smartphone gestures data for the constant verification of the user and periocular data for a second step verification module. The results executed over two datasets show a verification accuracy of 83% and 94% approximately, respectively for smartphone touch features and periocular data.


Continuous authentication Smartphone gesture data Periocular recognition 



Mirko Marras gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020, Axis III “Education and Training”, Thematic Goal 10, Priority of Investment 10ii, Specific Goal 10.5). The Italian Ministry of University, Education and Research (MIUR), partially supported this work, under the project ILEARNTV (announcement 391/2012, SMART CITIES AND COMMUNITIES AND SOCIAL INNOVATION).


  1. 1.
    Shen, H., Gao, C., He, D., Wu, L.: New biometrics-based authentication scheme for multi-server environment in critical systems. J. Ambient. Intell. Humaniz. Comput. 6(6), 825–834 (2015)CrossRefGoogle Scholar
  2. 2.
    Liu, Y., Ling, J., Liu, Z., Shen, J., Gao, C.: Finger vein secure biometric template generation based on deep learning. Soft Comput. 22(7), 2257–2265 (2018)CrossRefGoogle Scholar
  3. 3.
    Chen, Z., Wu, J., Castiglione, A., Wu, W.: Human continuous activity recognition based on energy-efficient schemes considering cloud security technology. Secur. Commun. Netw. 9(16), 3585–3601 (2016)CrossRefGoogle Scholar
  4. 4.
    Castiglione, A., Choo, K.K.R., Nappi, M., Narducci, F.: Biometrics in the cloud: challenges and research opportunities. IEEE Cloud Comput. 4(4), 12–17 (2017)CrossRefGoogle Scholar
  5. 5.
    Castiglione, A., Choo, K.K.R., Nappi, M., Ricciardi, S.: Context aware ubiquitous biometrics in edge of military things. IEEE Cloud Comput. 4(6), 16–20 (2017)CrossRefGoogle Scholar
  6. 6.
    Neves, J.C., Moreno, J.C., Barra, S., Proença, H.: Acquiring high-resolution face images in outdoor environments: a master-slave calibration algorithm. In: 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8, September 2015Google Scholar
  7. 7.
    Neves, J., Narducci, F., Barra, S., Proença, H.: Biometric recognition in surveillance scenarios: a survey. Artif. Intell. Rev. 46(4), 515–541 (2016)CrossRefGoogle Scholar
  8. 8.
    Neves, J.C., Santos, G., Filipe, S., Grancho, E., Barra, S., Narducci, F., Proença, H.: Quis-Campi: extending in the Wild biometric recognition to surveillance environments. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 59–68. Springer, Cham (2015). Scholar
  9. 9.
    Fenu, G., Marras, M., Boratto, L.: A multi-biometric system for continuous student authentication in e-learning platforms. Pattern Recognit. Lett. (2017)Google Scholar
  10. 10.
    Barra, S., Marsico, M.D., Galdi, C., Riccio, D., Wechsler, H.: Fame: face authentication for mobile encounter. In: 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, pp. 1–7, September 2013Google Scholar
  11. 11.
    Abate, A.F., Barra, S., Gallo, L., Narducci, F.: Kurtosis and skewness at pixel level as input for som networks to iris recognition on mobile devices. Pattern Recogn. Lett. 91, 37–43 (2017). Mobile Iris CHallenge Evaluation (MICHE-II)CrossRefGoogle Scholar
  12. 12.
    Dasgupta, D., Roy, A., Nag, A.: Advances in User Authentication. Springer, Heidelberg (2017)CrossRefGoogle Scholar
  13. 13.
    Schiavone, E., Ceccarelli, A., Bondavalli, A.: Continuous biometric verification for non-repudiation of remote services. In: Proceedings of the 12th International Conference on Availability, Reliability and Security, p. 4. ACM (2017)Google Scholar
  14. 14.
    Sanna, P.S., Marcialis, G.L.: Remote biometric verification for elearning applications: where we are. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10485, pp. 373–383. Springer, Cham (2017). Scholar
  15. 15.
    Fenu, G., Marras, M.: Leveraging continuous multi-modal authentication for access control in mobile cloud environments. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 331–342. Springer, Cham (2017). Scholar
  16. 16.
    Fathy, M.E., Patel, V.M., Chellappa, R.: Face-based active authentication on mobile devices. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1687–1691. IEEE (2015)Google Scholar
  17. 17.
    Muaaz, M., Mayrhofer, R.: An analysis of different approaches to gait recognition using cell phone based accelerometers. In: Proceedings of International Conference on Advances in Mobile Computing & Multimedia, p. 293. ACM (2013)Google Scholar
  18. 18.
    Ali, M.L., Monaco, J.V., Tappert, C.C., Qiu, M.: Keystroke biometric systems for user authentication. J. Signal Process. Syst. 86(2–3), 175–190 (2017)CrossRefGoogle Scholar
  19. 19.
    Teh, P.S., Zhang, N., Teoh, A.B.J., Chen, K.: A survey on touch dynamics authentication in mobile devices. Comput. Secur. 59, 210–235 (2016)CrossRefGoogle Scholar
  20. 20.
    Rahman, K.A., Moormann, R., Dierich, D., Hossain, M.S.: Continuous user verification via mouse activities. In: Dziech, A., Leszczuk, M., Baran, R. (eds.) MCSS 2015. CCIS, vol. 566, pp. 170–181. Springer, Cham (2015). Scholar
  21. 21.
    Shen, C., Chen, Y., Guan, X.: Performance evaluation of implicit smartphones authentication via sensor-behavior analysis. Inf. Sci. 430, 538–553 (2018)CrossRefGoogle Scholar
  22. 22.
    Zhang, H., Patel, V.M., Chellappa, R.: Robust multimodal recognition via multitask multivariate low-rank representations. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)Google Scholar
  23. 23.
    Sitová, Z., Šeděnka, J., Yang, Q., Peng, G., Zhou, G., Gasti, P., Balagani, K.S.: Hmog: new behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 11(5), 877–892 (2016)CrossRefGoogle Scholar
  24. 24.
    Mondal, S., Bours, P.: A study on continuous authentication using a combination of keystroke and mouse biometrics. Neurocomputing 230, 1–22 (2017)CrossRefGoogle Scholar
  25. 25.
    Fridman, L., Weber, S., Greenstadt, R., Kam, M.: Active authentication on mobile devices via stylometry, application usage, web browsing, and GPS location. IEEE Syst. J. 11(2), 513–521 (2017)CrossRefGoogle Scholar
  26. 26.
    Patel, V.M., Chellappa, R., Chandra, D., Barbello, B.: Continuous user authentication on mobile devices: recent progress and remaining challenges. IEEE Signal Process. Mag. 33(4), 49–61 (2016)CrossRefGoogle Scholar
  27. 27.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I. IEEE (2001)Google Scholar
  28. 28.
    Sitová, Z., et al.: Hmog: new behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 11(5), 877–892 (2016)CrossRefGoogle Scholar
  29. 29.
    Barra, S., Casanova, A., Fraschini, M., Nappi, M.: EEG/ECG signal fusion aimed at biometric recognition. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 35–42. Springer, Cham (2015). Scholar
  30. 30.
    Meinshausen, N.: Quantile regression forests. J. Mach. Learn. Res. 7, 983–999 (2006)MathSciNetzbMATHGoogle Scholar
  31. 31.
    Zhang, X., Jia, Y.: A linear discriminant analysis framework based on random subspace for face recognition. Pattern Recognit. 40(9), 2585–2591 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

Personalised recommendations