Advertisement

Student Authentication Method by Sequential Update of Face Information Registered in e-Learning System

  • Taisuke KawamataEmail author
  • Susumu Fujimori
  • Takako Akakura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9735)

Abstract

e-Learning is easing restrictions on time and space for a learner. However, its weak point is that a user authentication employs only on log-in with credentials, which makes it easy to cause a cheating. We have studied the changes in face image in e-Learning with the aim of detecting the cheating. We proposed an authentication method with sequential updates of student’s face information using new images taken by a web-camera during the e-Learning. We examined the update timing and procedure in this study, and found that the authentication accuracy the highest by summing each face feature vector in the face image which is taken when a student operates the e-Learning system.

Keywords

e-Learning Video lecture Student authentication Face image 

Notes

Acknowledgments

This work was supported in part by a Grant-in-Aid for Challenging Exploratory Research (No. 15K12427) from JSPS.

References

  1. 1.
    Suzuki, K.: e-Learning in Japan: past, present, and future. In: KAEM and the 4th BK21 GGRTE International Conference: Technology and Future Learning Space Proceedings, pp. 9–17 (2009)Google Scholar
  2. 2.
    Agulla, E., Rúa, E., Castro, J., Jiménez, D., Rifón, L.: Multimodal biometrics-based student attendance measurement in learning management systems. In: 11th IEEE International Symposium on Multimedia, pp. 699–704 (2009)Google Scholar
  3. 3.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Viola, P., Jones., M.: Rapid object detection using a boosted cascade of simple features. Proc. Comput. Vis. Pattern Recognit. 1, 511–518 (2001)Google Scholar
  5. 5.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distribution. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Taisuke Kawamata
    • 1
    Email author
  • Susumu Fujimori
    • 2
  • Takako Akakura
    • 2
  1. 1.Graduate School of EngineeringTokyo University of ScienceTokyoJapan
  2. 2.Faculty of EngineeringTokyo University of ScienceTokyoJapan

Personalised recommendations