CFP last date
20 May 2024
Reseach Article

Online Invigilation: A Holistic Approach

by Vaibhav Ahlawat, Ahirnish Pareek, S.k. Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 17
Year of Publication: 2014
Authors: Vaibhav Ahlawat, Ahirnish Pareek, S.k. Singh

Vaibhav Ahlawat, Ahirnish Pareek, S.k. Singh . Online Invigilation: A Holistic Approach. International Journal of Computer Applications. 90, 17 ( March 2014), 31-35. DOI=10.5120/15814-4673

@article{ 10.5120/15814-4673,
author = { Vaibhav Ahlawat, Ahirnish Pareek, S.k. Singh },
title = { Online Invigilation: A Holistic Approach },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 17 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { },
doi = { 10.5120/15814-4673 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:11:18.749877+05:30
%A Vaibhav Ahlawat
%A Ahirnish Pareek
%A S.k. Singh
%T Online Invigilation: A Holistic Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 17
%P 31-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Invigilation is an integral part of education and as education has evolved from conventional paper based methods to on-line ones, and so have the methods of invigilation. Major examinations are now online like TOEFL, GRE etc. But even with the assessment going online, invigilation still remains a manual affair; still officials have to be deployed on testing locations. Also in case of e-learning solutions the candidates are evaluated in their personal environment where there are no manual invigilators, thus a proper approach for online invigilation must be there. This paper aims to propose an invigilation model to automate the process and a tool for the same while taking into consideration the various constraints that come into picture for the specific scenario.

  1. P. Broadfoot and P. Black. Redefining assessment? The first ten years of assessment in education. Assessment in Education: Principles, Policy and Practice, Volume 11, Number 1, March 2004, pp. 7-26(20)
  2. N. Percival, J. Percival, C. Martins. The Virtual Invigilator: A Network-based Security System for Technology-Enhanced Assessments. In Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, October 22-24, 2008
  3. Software Secure. Remote Proctor. http://www. softwaresecure. com/solutions/remote-proctor. html
  4. Respondus. Respondus - Assessment Tools for Learning Systems. http://www. respondus. com/
  5. C. Yuan, Q. Yang. The Scheme of SIP-based Video Surveillance System. Second International Workshop on Education Technology and Computer Science, vol. 3, pp. 268-271, 2010.
  6. N. L Clarke, P. Dowland & S. M. Furnell. e-Invigilator: A Biometric-Based Supervision System for e-Assessments. International Conference on Information Society (i-Society), 2013.
  7. G. Pan, Z. Wu, and L. Sun. Liveness detection for face recognition. In K. Delac, M. Grgic, and M. S. Bartlett, editors, Recent Advances in Face Recognition, page Chapter 9. INTECH, 2008.
  8. K. Kollreider, H. Fronthaler, and J. Bigun. Non-intrusive liveness detection by face images. Image and Vision Computing, 27:233–244, 2009.
  9. W. Bao, H. Li, N. Li, and W. Jiang. A liveness detection method for face recognition based on optical flow field. In 2009 International Conference on Image Analysis and Signal Processing, pages 233–236. IEEE, 2009.
  10. J. Määttä, A. Hadid, and M. Pietikäinen. Face spoofing detection from single images using micro-texture analysis. In Proc. IJCB, 2011, pp. 1-7.
  11. Douglas A. Reynolds and Richard C. Rose. Robust Text-Independent Speaker Identification Using Gaussian Mixture speaker Models. IEEE Transactions on Speech and Audio Processing Vol-3, 1995
  12. P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman. Eigenfaces vs. Fisherfaces: recognition using class specific linear Projection. In IEEE transactions on pattern analysis and intelligence, 19(7), (1997).
  13. T. Ahonen, A. Hadid, and M. Pietikäinen. Face description with local binary patterns: Application to face recognition. In IEEE Trans. Pattern Anal. Mach. Intell. , 28:2037–2041, (2006).
  14. T. Ojala, M. Pietikäinen, D. Harwood, "A comparative study of texture measures with classification based on feature distributions. " In Pattern Recognition 29 (1996) 51–59.
  15. H. Bay, A. Ess, T. Tuytelaars and L. Van Gool. Speeded-up robust features (SURF). In Comput. Vis. Image Underst. , 110(3), 346-359 (2008).
  16. P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR (1). (2001) 511 – 518.
  17. X. Tan, Y. Li, J. Liu, and L. Jiang. Face liveness detection from a single image with sparse low rank bilinear discriminative model. In Proceedings of the 11th European conference on Computer vision: Part VI, ECCV'10, pages 504–517, Berlin, Heidelberg, 2010. Springer-Verlag.
Index Terms

Computer Science
Information Sciences


e-Invigilation assessment authentication monitoring system cheating.