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Walking on the Cloud: Gait Recognition, a Wearable Solution

  • Aniello Castiglione
  • Kim-Kwang Raymond Choo
  • Maria De MarsicoEmail author
  • Alessio Mecca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11058)

Abstract

Biometrics and cloud computing are converging towards a common application context aiming at deploying biometric authentication as a remote service (Biometrics as a Service - BaaS). The advantages for the final user is to be relieved from the burden related to acquire/maintain specific software, and to gain the ability of building personalized applications where biometric services can be embedded through suitable cloud APIs. Gait is one of the promising biometric traits that can be investigated in this scenario. In particular, this paper deals with the processing techniques based on wearable sensors, e.g., accelerometers. These sensors are nowadays ubiquitous in mobile devices, and allow the acquisition of lightweight signals that can be sent remotely for processing. As an example of possible applications, a positive recognition may automatically allow access to restricted zones without an explicit action by the user, that has just to approach the entrance walking normally.

Keywords

Gait recognition Wearable sensors Cloud services Biometrics as a Service BaaS 

References

  1. 1.
    Abate, A.F., Nappi, M., Ricciardi, S.: I-am: implicitly authenticate me person authentication on mobile devices through ear shape and arm gesture. IEEE Trans. Syst. Man Cybern. Syst. 99, 1–13 (2017)CrossRefGoogle Scholar
  2. 2.
    Barra, S., De Marsico, M., Nappi, M., Narducci, F., Riccio, D.: A hand-based biometric system in visible light for mobile environments. Inf. Sci. (2018)Google Scholar
  3. 3.
    Castiglione, A., Santis, A.D., Masucci, B., Palmieri, F., Castiglione, A., Huang, X.: Cryptographic hierarchical access control for dynamic structures. IEEE Trans. Inf. Forensics Secur. 11(10), 2349–2364 (2016).  https://doi.org/10.1109/TIFS.2016.2581147CrossRefzbMATHGoogle Scholar
  4. 4.
    Castiglione, A., et al.: Hierarchical and shared access control. IEEE Trans. Inf. Forensics Secur. 11(4), 850–865 (2016).  https://doi.org/10.1109/TIFS.2015.2512533CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Cinque, M., Russo, S., Esposito, C., Choo, K.K.R., Free-Nelson, F., Kamhoua, C.A.: Cloud reliability: possible sources of security and legal issues? IEEE Cloud Comput. 5(3), 31–38 (2018)CrossRefGoogle Scholar
  7. 7.
    De Marsico, M., De Pasquale, D., Mecca, A.: Embedded accelerometer signal normalization for cross-device gait recognition. In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–5. IEEE (2016)Google Scholar
  8. 8.
    De Marsico, M., Mecca, A.: Biometric walk recognizer. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 19–26. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23222-5_3CrossRefGoogle Scholar
  9. 9.
    De Marsico, M., Mecca, A.: Biometric walk recognizer. Multimedia Tools Appl. 76(4), 4713–4745 (2017)CrossRefGoogle Scholar
  10. 10.
    De Marsico, M., Nappi, M., Narducci, F., Proença, H.: Insights into the results of miche I-mobile iris challenge evaluation. Pattern Recogn. 74, 286–304 (2018)CrossRefGoogle Scholar
  11. 11.
    De Marsico, M., Nappi, M., Proença, H.: Results from miche II-mobile iris challenge evaluation II. Pattern Recogn. Lett. 91, 3–10 (2017)CrossRefGoogle Scholar
  12. 12.
    De Marsico, M., Nappi, M., Riccio, D., Wechsler, H.: Mobile iris challenge evaluation (miche)-I, biometric iris dataset and protocols. Pattern Recogn. Lett. 57, 17–23 (2015)CrossRefGoogle Scholar
  13. 13.
    De Marsico, M., Nemmi, E., Prenkaj, B., Saturni, G.: A smart peephole on the cloud. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 364–374. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70742-6_34CrossRefGoogle Scholar
  14. 14.
    De Marsico, M., Nemmi, E., Prenkaj, B., Saturni, G.: House in the (biometric) cloud: a possible application. IEEE Cloud Comput. 5(4), 58–69 (2018)CrossRefGoogle Scholar
  15. 15.
    Derawi, M.O., Nickel, C., Bours, P., Busch, C.: Unobtrusive user-authentication on mobile phones using biometric gait recognition. In: 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 306–311. IEEE (2010)Google Scholar
  16. 16.
    Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mobile Comput. 13(18), 1587–1611 (2013)CrossRefGoogle Scholar
  17. 17.
    Gafurov, D., Snekkenes, E.: Towards understanding the uniqueness of gait biometric. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2008, pp. 1–8. IEEE (2008)Google Scholar
  18. 18.
    Gafurov, D., Snekkenes, E., Bours, P.: Improved gait recognition performance using cycle matching. In: 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 836–841. IEEE (2010)Google Scholar
  19. 19.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 4–20 (2000)CrossRefGoogle Scholar
  20. 20.
    Muaaz, M., Mayrhofer, R.: Smartphone-based gait recognition: from authentication to imitation. IEEE Trans. Mobile Comput. 16(11), 3209–3221 (2017)CrossRefGoogle Scholar
  21. 21.
    Neverova, N., et al.: Learning human identity from motion patterns. IEEE Access 4, 1810–1820 (2016)CrossRefGoogle Scholar
  22. 22.
    Nickel, C., Brandt, H., Busch, C.: Classification of acceleration data for biometric gait recognition on mobile devices. BIOSIG 11, 57–66 (2011)Google Scholar
  23. 23.
    Nickel, C., Busch, C., Rangarajan, S., Möbius, M.: Using hidden markov models for accelerometer-based biometric gait recognition. In: 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications (CSPA), pp. 58–63. IEEE (2011)Google Scholar
  24. 24.
    Nickel, C., Wirtl, T., Busch, C.: Authentication of smartphone users based on the way they walk using K-NN algorithm. In: 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pp. 16–20. IEEE (2012)Google Scholar
  25. 25.
    Nowlan, M.F.: Human identification via gait recognition using accelerometer gyro forces. Yale Computer Science (2009). http://www.cs.yale.edu/homes/mfn3/pub/mfngaitid.pdf. Accessed 12 Nov 2013
  26. 26.
    Pan, G., Zhang, Y., Wu, Z.: Accelerometer-based gait recognition via voting by signature points. Electr. Lett. 45(22), 1116–1118 (2009)CrossRefGoogle Scholar
  27. 27.
    Rimal, B.P., Choi, E., Lumb, I.: A taxonomy and survey of cloud computing systems. In: Fifth International Joint Conference on INC, IMS and IDC, NCM 2009, pp. 44–51. IEEE (2009)Google Scholar
  28. 28.
    Schmidt, A.: Implicit human computer interaction through context. Pers. Technol. 4(2–3), 191–199 (2000)CrossRefGoogle Scholar
  29. 29.
    Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34(1), 1–11 (2011)CrossRefGoogle Scholar
  30. 30.
    Wilder, B.: Cloud Architecture Patterns: Using Microsoft Azure. O’Reilly Media Inc., Sebastopol (2012)Google Scholar
  31. 31.
    Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., Wu, Z.: Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Trans. Cybern. 45(9), 1864–1875 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of SalernoSalernoItaly
  2. 2.The University of Texas at San AntonioSan AntonioUSA
  3. 3.Sapienza University of RomeRomeItaly

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