A Virtual Proctor with Biometric Authentication for Facilitating Distance Education

  • Zhou Zhang
  • El-Sayed Aziz
  • Sven EscheEmail author
  • Constantin Chassapis
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 22)


The lack of efficient and reliable proctoring for tests, examinations and laboratory exercises is slowing down the adoption of distance education. At present, the most popular solution is to arrange for proctors to supervise the students through a surveillance camera system. This method exhibits two shortcomings. The cost for setting up the surveillance system is high and the proctoring process is laborious and tedious. In order to overcome these shortcomings, some proctoring software that identifies and monitors student behavior during educational activities has been developed. However, these software solutions exhibit certain limitations: (i) They impose more severe restrictions on the students than a human proctor would. The students have to sit upright and remain directly in front of their webcams at all times. (ii) The reliability of these software systems highly depends on the initial conditions under which the educational activity is started. For example, changes in the lighting conditions can cause erroneous results.

In order to improve the usability and to overcome the shortcomings of the existing remote proctoring methods, a virtual proctor (VP) with biometric authentication and facial tracking functionality is proposed here. In this paper, a two-stage approach (facial detection and facial recognition) for designing the VP is introduced. Then, an innovative method to crop out the face region from images based on facial detection is presented. After that, in order to render the usage of the VP more comfortable to the students, in addition to an eigenface-based facial recognition algorithm, a modified facial recognition method based on a real-time stereo matching algorithm is employed to track the students’ movements. Then, the VP identifies suspicious student behaviors that may represent cheating attempts. By employing a combination of eigenface-based facial recognition and real-time stereo matching, the students can move forward, backward, left, right and can rotate their head in a larger range. In addition, the modified algorithm used here is reliable to changes of lighting, thus decreasing the possibility of false identification of suspicious behaviors.


Distance education Virtual proctor Face detection Facial recognition Stereo matching 


  1. 1.
  2. 2.
    Zhang, Z., Zhang, M., Chang, Y., Esche, S.K., Chassapis, C.: A smart method for developing game-based virtual laboratories. In: Proceedings of the ASME International Mechanical Engineering Conference and Exposition, IMECE 2015, Houston, Texas, 13–19 November 2015Google Scholar
  3. 3.
    Zhang, Z., Zhang, M., Chang, Y., Esche, S.K., Chassapis, C.: Real-time 3D reconstruction for facilitating the development of game-based virtual laboratories. Comput. Educ. J. 7(1), 85–99 (2016)Google Scholar
  4. 4.
    Zhang, Z., Zhang, M., Tumkor, S., Chang, Y., Esche, S.K., Chassapis, C.: Integration of physical devices into game-based virtual reality. Int. J. Online Eng. 9, 25–38 (2013)CrossRefGoogle Scholar
  5. 5.
    Qureshi, F., Terzopoulos, D.: Smart camera networks in virtual reality. In: Proceedings of First ACM/IEEE International Conference on Distributed Smart Cameras, Vienna, Austria, 25–28 September 2007Google Scholar
  6. 6. Accessed Oct 2016
  7. 7. Accessed Oct 2016
  8. 8.
  9. 9. Accessed Oct 2016
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Rasmussen, K.B., Roeschlin, M., Martinovic, I., Tsudik, G.: Authentication using pulse-response biometrics. In: Proceedings of Network and Distributed System Security Symposium 2014, San Diego, California, USA, 23–25 February 2014Google Scholar
  14. 14.
    Bača, M., Grd, P., Fotak, T.: Basic principles and trends in hand geometry and hand shape biometrics. In: New Trends and Developments in Biometrics. INTECH Open Access Publisher (2012) Google Scholar
  15. 15.
  16. 16.
    Proctor, R.W., Lien, M.C., Salvendy, G., Schultz, E.E.: A task analysis of usability in third-party authentication. Inf. Secur. Bull. 5(3), 49–56 (2000)Google Scholar
  17. 17.
  18. 18.
    Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proceedings of 4th Asian Conference on Computer Vision, Taipei, Taiwan, 5–8 January 2000Google Scholar
  19. 19.
  20. 20.
    Fröba, B., Külbeck, C.: Real-time face detection using edge-orientation matching. In: Proceedings of International Conference on Audio- and Video-Based Biometric Person Authentication, Halmstad, Sweden, 6–8 June 2001Google Scholar
  21. 21.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. In: Proceedings of International Conference on Audio- and Video-Based Biometric Person Authentication, Halmstad, Sweden, 6–8 June 2001Google Scholar
  22. 22.
    Vasconcelos, N., Saberian, M.J.: Boosting classifier cascades. In: Proceedings of Advances in Neural Information Processing Systems 23, Vancouver, British Columbia, Canada, 6–9 December 2010Google Scholar
  23. 23.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: Proceedings of Advances in Neural Information Processing System 14, Vancouver, British Columbia, Canada, 3–8 December 2001Google Scholar
  24. 24.
    Gokturk, S.B., Bouguet, J.Y., Tomasi, C., Girod, B.: Model-based face tracking for view-independent facial expression recognition. In: Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., USA, 20–21 May 2002Google Scholar
  25. 25.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  26. 26.
    Wilson, P.I., Fernandez, J.: Facial feature detection using Haar classifiers. J. Comput. Sci. Coll. 21(4), 127–133 (2006)Google Scholar
  27. 27.
  28. 28. Accessed Nov 2016
  29. 29.
    Zhang, Z., Zhang, M., Chang, Y., Esche, S.K., Chassapis, C.: A virtual laboratory system with biometric authentication and remote proctoring based on facial recognition. In: Proceedings of the 2016 ASEE Annual Conference and Exposition, New Orleans, LA, USA, 26–29 June 2016Google Scholar
  30. 30.
  31. 31.
  32. 32.
    Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1042–1052 (1993)CrossRefGoogle Scholar
  33. 33.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  34. 34.
    Jafri, R., Arabnia, H.: A survey of face recognition techniques. J. Inf. Proces. Syst. 5(2), 41–68 (2009)CrossRefGoogle Scholar
  35. 35.
    Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, 21–23 June 1994Google Scholar
  36. 36.
  37. 37.
    Menezes, P., Barreto, J.C., Dias, J.: Face tracking based on Haar-like features and eigenfaces. In: Proceedings of IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Técnico, Lisboa, Portugal, 5–7 July 2004Google Scholar
  38. 38.
  39. 39.
    Graham, D.B., Allinson, N.M.: Characterising virtual eigensignatures for general purpose face recognition. face recognition, pp. 446–456. Springer, Heidelberg (1998). doi: 10.1007/978-3-642-72201-1_25 CrossRefGoogle Scholar
  40. 40.
  41. 41.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)CrossRefGoogle Scholar
  42. 42.
  43. 43.
    Zhang, Z., Zhang, M., Chang, Y., Esche, S.K., Chassapis, C.: A virtual laboratory combined with biometric authentication and 3D reconstruction. In: Proceedings of the ASME International Mechanical Engineering Conference and Exposition, IMECE 2016, Phoenix, Arizona, USA, 11–17 November 2016Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Zhou Zhang
    • 1
  • El-Sayed Aziz
    • 1
  • Sven Esche
    • 1
    Email author
  • Constantin Chassapis
    • 1
  1. 1.Department of Mechanical EngineeringStevens Institute of TechnologyHobokenUSA

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