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Behavioral Biometrics Based on Human-Computer Interaction Devices

  • Chi LinEmail author
  • Mohammad S. Obaidat
Chapter

Abstract

The purpose of this chapter is to describe a new approach to recognize the identity of a person through analyzing the behavioral biometrics in Wi-Fi signals and their potential application prospects. A solid understanding of processing Wi-Fi signals helps to interpret solid information and problem statement on identity recognition through Wi-Fi signals. The ubiquitous and temporal features of Wi-Fi signals are the basis of recognition and localization. We introduce a new paradigm on how to use Wi-Fi signals to identify the human in the open environment. We proposed Wide, a Wi-Fi signal-based human identity recognition system. First, we describe the components of Wide and how it works in detail. Through collecting CSI (channel state information) profiles, Wide is able to recognize the human identity through sampling and extracting features of the received Wi-Fi signals. Then, to reduce the storage overhead while guaranteeing high recognizing accuracy, principal component analysis (PCA) technique is used. Finally, test-bed experiments are conducted to show the performance of Wide, indicating that Wide can quickly recognize people in a high accuracy.

The chapter starts with the definition of Wi-Fi signals and CSI (channel state information) and behavioral biometrics-related applications. Particular emphasis is placed on the characteristic of the CSI, which indicates that CSI can be used for recognizing the identity of people. Then we highlight our objective and demonstrate our design in detail. At last, experiments are conducted through collecting, analyzing, and processing Wi-Fi signals to recognize the identity of people, revealing that the proposed scheme can recognize people with promising accuracy in a short time.

This chapter is structured as follows. Section 1 focuses on research background in behavioral biometrics and illustrations on characteristics of key technologies. Section 2 gives a brief overview on related achievement in this research field. Section 3 looks at the essence of related theory and behavioral biometric recognition methods. Section 4 deals with experimental installations and configurations. Section 5 analyzes the experimental results and discusses the potential features of our scheme. Section 6 concludes this chapter and outlines future research trends in Wi-Fi signal topics.

Reference

  1. 1.
    S. Sigg, S. Shi, F. Buesching, Y. Ji, L Wolf, Leveraging Rf-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features, in Proceedings of International Conference on Advances in Mobile Computing & Multimedia, (ACM, 2013), page 43Google Scholar
  2. 2.
    S. Sigg, M. Scholz, S. Shi, Y. Ji, M. Beigl, Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE Trans. Mob. Comput. 13(4), 907–920 (2014)CrossRefGoogle Scholar
  3. 3.
    Z. Yang, Z. Zhou, Y. Liu, From RSSI to CSI: Indoor localization via channel response. Acm Comput. Surv. 46(2), 25 (2013)CrossRefGoogle Scholar
  4. 4.
    D. Halperin, W. Hu, A. Sheth, D. Wetherall, Tool release: gathering 802.11n traces with channel state information. ACM SIGCOMM CCR 41(1), 53 (2011)CrossRefGoogle Scholar
  5. 5.
    S. Sen, J. Lee, K.-H. Kim, P. Congdon, Avoiding multipath to revive in building WiFi localization. in Proceeding of ACM MobiSys, (2013), pp. 249–262Google Scholar
  6. 6.
    K. Ali, A.X. Liu, W. Wang, M. Shahzad, Keystroke recognition using WiFi signals, in Proceedings of ACM MobiCom, (2015), pp. 90–102Google Scholar
  7. 7.
    P. Van Dorp, F. Groen, Feature-based human motion parameter estimation with radar. IET Radar Sonar. Nav. 2(2), 135–145 (2008)CrossRefGoogle Scholar
  8. 8.
    Q. Pu, S. Gupta, S. Gollakota, Shwetak Patel, Whole-home gesture recognition using wireless signals, in Proceedings of the 19th annual international conference on Mobile computing & networking, (ACM, 2013), pp. 27–38Google Scholar
  9. 9.
    F. Adib, Z. Kabelac, D. Katabi, R. Miller. 3d tracking via body radio reflections, in Usenix NSDI, vol. 14, (2013)Google Scholar
  10. 10.
    Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, H. Liu. E-eyes: In-home device-free activity identification using fine-grained WiFi signatures, in Proceedings of ACM MobiCom, (2014)Google Scholar
  11. 11.
    K. Ali, A.X. Liu, W. Wang, M. Shahzad, Keystroke recognition using WiFi signals, (ACM MobiCom, 2015), pp. 90–102Google Scholar
  12. 12.
    Y. Wang, K. Wu, L.M. Ni, Wifall: device-free fall detection by wireless networks[J]. IEEE Trans. Mob. Comput. 16(2), 581–594 (2017)CrossRefGoogle Scholar
  13. 13.
    G. Wang, Y. Zou, Z. Zhou, et al., We can hear you with wi-fi[J]. IEEE Trans. Mob. Comput. 15(11), 2907–2920 (2016)CrossRefGoogle Scholar
  14. 14.
    D. Halperin, W. Hu, A. Sheth, et al., Tool release: gathering 802.11 n traces with channel state information[J]. ACM SIGCOMM Comput. Commun. Rev. 41(1), 53–53 (2011)CrossRefGoogle Scholar
  15. 15.
    K. Wu, J. Xiao, Y. Yi, et al., CSI-based indoor localization[J]. IEEE Trans. Parallel Distrib. Syst. 24(7), 1300–1309 (2013)CrossRefGoogle Scholar
  16. 16.
    K. Wu, J. Xiao, Y. Yi, et al., Fila: fine-grained indoor localization[C], in INFOCOM, 2012 Proceedings IEEE, (IEEE, 2012), pp. 2210–2218Google Scholar
  17. 17.
    X. Liu, J. Cao, S. Tang, et al., Wi-Sleep: contactless sleep monitoring via WiFi signals[C], in Real-Time Systems Symposium (RTSS), 2014 IEEE, (IEEE, 2014), pp. 346–355Google Scholar
  18. 18.
    X. Wang, L. Gao, S. Mao, et al., DeepFi: deep learning for indoor fingerprinting using channel state information[C], in Wireless Communications and Networking Conference (WCNC), 2015 IEEE, (IEEE, 2015), pp. 1666–1671Google Scholar
  19. 19.
    W. Wang, A.X. Liu, M. Shahzad, et al., Understanding and modeling of wifi signal based human activity recognition[C], in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, (ACM, 2015), pp. 65–76Google Scholar
  20. 20.
    S. He, S.H.G. Chan, Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons [J]. IEEE Commun. Surveys Tuts. 18(1), 466–490 (2016)CrossRefGoogle Scholar
  21. 21.
    J. Han, C. Qian, X. Wang, et al., Twins: device-free object tracking using passive tags[J]. IEEE/ACM Trans. Networking 24(3), 1605–1617 (2016)CrossRefGoogle Scholar
  22. 22.
    K. Ali, A.X. Liu, W. Wang, et al., Keystroke recognition using wifi signals[C], in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, (ACM, 2015), pp. 90–102Google Scholar
  23. 23.
    Y. Wen, X. Tian, X. Wang, et al., Fundamental limits of RSS fingerprinting based indoor localization[C], in Computer Communications (INFOCOM), 2015 I.E. Conference on. IEEE, (2015), pp. 2479–2487Google Scholar
  24. 24.
    Z. Zhou, Z. Yang, C. Wu, et al., Lifi: line-of-sight identification with wifi[C], in INFOCOM, 2014 Proceedings IEEE, (IEEE, 2014), pp. 2688–2696Google Scholar
  25. 25.
    B. Wei, W. Hu, M. Yang, et al., Radio-based device-free activity recognition with radio frequency interference[C], in Proceedings of the 14th International Conference on Information Processing in Sensor Networks, (ACM, 2015), pp. 154–165Google Scholar
  26. 26.
    Z.P. Jiang, W. Xi, X. Li, et al., Communicating is crowdsourcing: Wi-Fi indoor localization with CSI-based speed estimation[J]. J. Comput. Sci. Technol. 29(4), 589–604 (2014)CrossRefGoogle Scholar
  27. 27.
    X. Wang, L. Gao, S. Mao, et al., CSI-based fingerprinting for indoor localization: a deep learning approach[J]. IEEE Trans. Veh. Technol. 66(1), 763–776 (2017)Google Scholar
  28. 28.
    C. Wu, Z. Yang, Z. Zhou, et al., Non-invasive detection of moving and stationary human with WiFi[J]. IEEE J. Sel. Areas Commun. 33(11), 2329–2342 (2015)CrossRefGoogle Scholar
  29. 29.
    Xu H, Yang Z, Zhou Z, et al. Enhancing wifi-based localization with visual clues[C]. in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. (ACM 2015), pp. 963–974Google Scholar
  30. 30.
    N.U. Hassan, A. Naeem, M.A. Pasha, et al., Indoor positioning using visible led lights: a survey[J]. ACM Comput. Surv. (CSUR) 48(2), 20 (2015)CrossRefGoogle Scholar
  31. 31.
    A. Makki, A. Siddig, M. Saad, et al., Survey of WiFi positioning using time-based techniques[J]. Comput. Netw. 88, 218–233 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software, Dalian University of TechnologyDalianChina
  2. 2.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  3. 3.Department of Computer and Information ScienceFordham UniversityBronxUSA

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