Attention Control during Distance Learning Sessions
The distance learning (DL) is a teaching system that extends the education beyond the physical barriers, providing access to remote places and disabilities. The increasing need of procedures for DL certification is now involving biometric approach. An analysis of biometric techniques is shown in order to ensure the users authentication, to verify the individual’s attention level and then to certificate the learning outcomes. That is necessary to implement a system to identify uniquely the users and to track both path’s carried (visited pages) and use’s time, to have a secure users identification and also validation of the environments conditions in which they take place during possible tests of certification. The appropriate biometric technique is appeared the Face Recognition because it allows a real-time verification of the real presence, low implementation costs by use of webcam and reasonable degree of reliability. To avoid the influence related to environmental conditions, it has been realized a modular system that implements Detection and Recognition operations. The implemented system is able to verify the presence of learners beyond the screen during lessons or learning tests, to allow authentication and to verify the simultaneous presence of other individuals in order to start an alarm if unregistered peoples are present during learning or testing sessions. This system is also capable to recognize the attention level of users through Request Random Windows (RRW). The application opens casually a RRW in different screen position during the DL and asks learner to click upon to close it within a few seconds. When this window is closed, a new step of Face Recognitions is performed again to validate the presence of the same user. Interesting results are obtained in experimental cases employing these techniques on a individuals samples set.
Keywordsattention control distance learning biometric techniques
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- 2.Kanade, D.: Computer Recognition of Human Faces. Birkauser, Basel and Stuttgart (1997)Google Scholar
- 3.Reifeld, T., Yeshurun, Y.: Robust Detection of Facial Features by Generalized Symmetry. In: Proceedings of 11th International Conference on Pattern Recognition (1992)Google Scholar
- 4.Bradski, G., Kaehler, A.: Learning: OpenCV. Computer Vision with the OpenCV Library. O’Reilly Media (2008)Google Scholar
- 5.Padilla, R., Costa Filho, C.F.F., Costa, M.G.F.: Evaluation of Haar Cascade Classifiers Designed for Face Detection. In: World Academy of Science, Engineering and Technology (2012)Google Scholar