Advertisement

Seeing Is Believing: Authenticating Users with What They See and Remember

  • Wayne Chiu
  • Kuo-Hui Yeh
  • Akihito Nakamura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11125)

Abstract

Brainwaves, as external signals of a functioning brain, provide a possible glimpse into how we think and react. However, seen another way, we could reasonably expect that a given action or event could be linked back to its corresponding brainwave reaction. Recently, commercial products in the form of commercial brainwave headsets have flooded into the market, opening up the possibility of exploiting brainwaves for various purposes and making this more feasible. In this paper, we build an authentication system based on brainwave reactions to a chain of events. We use a commercially available brainwave headset to collect brainwave data of participants for use in the proposed authentication system. After the brainwave data collection process, we apply a machine learning-based approach to extract features from brainwaves to serve as authentication tokens in the system and to support the authentication system itself.

Keywords

Authentication Brainwave Wearable Machine learning 

References

  1. 1.
    Google SmartLock. https://get.google.com/smartlock/. Accessed 12 Apr 2018
  2. 2.
    How to take advantage of the Dynamic Lock feature in Windows 10. https://www.techrepublic.com/article/how-to-take-advantage-of-.the-dynamic-lock-feature-in-windows-10/. Accessed 12 Apr 2018
  3. 3.
    Eye Closed Brainwave Dataset. http://www.bri.com.tw/data/sample_data/BR8_sample%20data_eyeclosed20141205.rar. Accessed 12 Apr 2018
  4. 4.
    Zhou, L.: You think, therefore you are: transparent authentication system with brainwave-oriented bio-features for IoT networks. IEEE Trans. Emerg. Top. Comput. (Early Access)Google Scholar
  5. 5.
    Brain Partition. http://www.bri.com.tw/. Accessed 12 Apr 2018
  6. 6.
    Yeh, K.H.: I walk, therefore i am: continuous user authentication with plantar biometrics. IEEE Commun. Mag. 56, 150–157 (2018)CrossRefGoogle Scholar
  7. 7.
    Matsuyama, Y.: Brain signal’s low-frequency fits the continuous authentication. Neurocomputing 164, 137–143 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Dong Hwa UniversityHualienTaiwan
  2. 2.The University of AizuAizuwakamatsuJapan

Personalised recommendations