Smart Kiosk with Gait-Based Continuous Authentication

  • Duong-Tien PhanEmail author
  • Nhan Nguyen-Trong Dam
  • Minh-Phuc Nguyen
  • Minh-Triet Tran
  • Toan-Thinh Truong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9189)


The authors propose to develop a smart kiosk that plays the role of an identity selector activated implicitly when a user is approaching that kiosk. The identity of a user is recognized implicitly in background by a mobile/wearable device based on his or her gait features. Upon arriving at a smart kiosk, the authentication process is performed automatically with the current available user identity in his or her portable device. To realize our system, we propose a new secure authentication scheme compatible with gait-based continuous authentication that can resist against known attacks, including three-factor attacks. Furthermore, we also propose a method to recognize users from their moving patterns using multiple SVM classifiers. Experiments with a dataset with 38 people show that this method can achieve the accuracy up to 92.028 %.


Gait-based recognition Continuous authentication Smart kiosk Mobile device Wearable device 



This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number B2015-18-01.


  1. 1.
    Lamport, L.: Password authentication with insecure communication. Commun. ACM 24(11), 770–772 (1981)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Lee, C.C., Hwang, M.S., Liao, I.E.: Security enhancement on a new authentication scheme with anonymity for wireless environments. IEEE Trans. Industr. Electron. 53(5), 1683–1686 (2006)CrossRefGoogle Scholar
  3. 3.
    Yang, G., Wong, D.S., Wang, H., Deng, X.: Two-factor mutual authentication based on smart cards and passwords. J. Comput. Syst. Sci. 74(7), 1160–1172 (2008)CrossRefMathSciNetzbMATHGoogle Scholar
  4. 4.
    An, Y.: Security analysis and enhancements of an effective biometric-based remote user authentication scheme using smart cards. J. Biomed. Biotechnol. 2012(519723), 6 (2012)Google Scholar
  5. 5.
    Khan, M.K., Kumari, S.: (An improved biometrics-based remote user authentication scheme with user anonymity. J. Biomed. Biotechnol. 2013(491289), 9 (2013)Google Scholar
  6. 6.
    Sarvabhatla, M., Giri, M., Vorugunti, C.S.: A secure biometrics-based remote user authentication scheme for secure data exchange. Embed. Syst. 2014, 110–115 (2014)Google Scholar
  7. 7.
    Wen, F., Susilo, W., Yang, G.: Analysis and improvement on a biometric-based remote user authentication scheme using smart cards. J. Wireless Pers. Commun. 80(4), 1747–1760 (2014)CrossRefGoogle Scholar
  8. 8.
    Fan, C.I., Lin, Y.H.: Provably secure remote truly three-factor authentication scheme with privacy protection on biometrics. IEEE Trans. Inf. Forensics Secur. 4(4), 933–945 (2009)CrossRefGoogle Scholar
  9. 9.
    Thinh, T-T., Tran, M-T., Duong, A-D.: Robust mobile device integration of a fingerprint biometric remote authentication scheme. In: 26th IEEE International Conference on Advanced Information Networking and Applications (AINA 2012), pp. 678–685 (2012)Google Scholar
  10. 10.
    Thinh, T-T., Tran, M-T., Duong, A-D.: Robust secure dynamic ID based remote user authentication scheme for multi-server environment. In: 13th International Conference on Computational Science and Its Applications (ICCSA 2013). LNCS, vol. 7975, pp. 502–515 (2013)Google Scholar
  11. 11.
    Pan, G., Zhang, Y., Wu, Z.: Accelerometer-based gait recognition via voting by signature points. IET Electron. Lett. 45(22), 1116–1118 (2009)CrossRefGoogle Scholar
  12. 12.
    Frank, F., Mannor, S., Precup, D.: Activity and gait recognition with time-delay embeddings. In: The 24th AAAI Conference on Artificial Intelligence 2010, pp. 1581–1586 (2010)Google Scholar
  13. 13.
    Dandachi, G., Hassan, B.E., Hussein, A.E.: A novel identification/verification model using smartphone’s sensors and user behavior. In: 2nd International Conference on Advances in Biomedical Engineering (ICABME 2013), pp. 235–238 (2013)Google Scholar
  14. 14.
    Nickel, C., Busch, C.: Classifying accelerometer data via hidden Markov models to authenticate people by the way they walk. IEEE Aerosp. Electron. Syst. Mag. 28(10), 29–35 (2013)CrossRefGoogle Scholar
  15. 15.
    Hoang, T., Choi, D., Vo, V., Nguyen, A., Nguyen, T.: A lightweight gait authentication on mobile phone regardless of installation error. In: The 28th IFIP TC 11 International Conference (SEC 2013), pp. 83–101 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Duong-Tien Phan
    • 1
    Email author
  • Nhan Nguyen-Trong Dam
    • 1
  • Minh-Phuc Nguyen
    • 1
  • Minh-Triet Tran
    • 1
  • Toan-Thinh Truong
    • 1
  1. 1.Faculty of Information TechnologyUniversity of Science VNU-HCMHo Chi Minh CityVietnam

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