Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner's

  • El-Kaber Hachem
  • Moulay Hachem Harouni Alaou
Keywords: Cloud, J48, Facial Image, Learning Platform, LSH

Abstract

As part of our project which aims at the realization of a system named ASTEMOI. In this article, we display a new and productive facial image representation based on the Local Sensitive Hash (LSH). This technique makes it possible to recognize the learners who follow their training in our learning platform. Once recognized, the student must be oriented towards an appropriate profile that takes into account his strengths and weaknesses. We also use a light processing module on the client device with a compact code so that we don’t need a lot of bandwidth, a lot of network transmission capacity to send the feature over the network, and to be able to index many pictures in a huge database in the cloud.

Downloads

Download data is not yet available.

Author Biographies

El-Kaber Hachem

Department of Physics, Faculty of Sciences, Moulay Ismail University. Meknes, Morocco.

Moulay Hachem Harouni Alaou

Research Team EDP and Scientific Computing, Mathematics and Computer Department, Faculty of Science, Moulay Ismail University. Meknes, Morocco.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
        Views : 710
2019-12-03
    Downloads : 226
How to Cite
[1]
E.-K. Hachem and M. H. Harouni Alaou, “Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner’s ”, International Journal of Artificial Intelligence, vol. 6, no. 2, pp. 112-119, Dec. 2019.
Section
Articles

References

H. H. Alaoui, E. -K. Hachem, and C. Ziti, “Artificial Intelligence in e-Learning, chapter of Textbook: Shaping the Future of ICT: Trends,” in Information Technology, Communication Engineering and Knowledge, published by CRC Press Taylor, 2017.

H. H. Alaoui, E. -K. Hachem, and C. Ziti, “Artificial Intelligence,” in e-Learning, Communication, Management and Information Technology – Sampaio de Alencar (Ed.), pp. 145-150. London: Taylor & Francis Group, 2017.

X. Ge, X. Huang, Y. Wang, M. Chen, Q. Li, T. Han and C. X. Wang, “Energy efficiency optimization for mimo-ofdm mobile multimedia communication systems with qos constraints,” IEEE Trans on Vehicular Technology, vol. 63, no. 5, pp. 2127–2138, 2014.

M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.

K. Etemad, and R. Chellappa, “Discriminant Analysis for Recognition Of Human Face Images,” J. Optical Soc. Am, vol. 14, pp. 1724-1733, 1997.

J. Yang, D. Zhang, A. F. Frangi and J. Yang, “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, 2004.

O. Arandjelovic, and A. Zisserman, “Who Are You? - Learning Person Specific Classifiers from Video,” in Proceedings of the CVPR, pp. 1145–1152, 2009.

J. Ji, S. Yan, J. Li, L. Gao, Q. Tian and B. Zhang, Batch-Orthogonal Locality-Sensitive Hashing for Angular Similarity,” IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 36, no. 10, pp. 1963–1974, 2014.

X. Yuan, and S. Yan, Visual Classification with Multi-Task Joint Sparse Representation,” in Proceedings of CVPR, pp. 3493–3500, 2010.

B. Kavitha, S. Karthikeyan and B. Chitra, “Efficient Intrusion Detection with Reduced Dimension Using Data Mining Classification Methods and Their Performance Comparison,” BAIP 2010, pp. 96-101, 2010.

M. H. Danham, and S. Sridhar, Data Mining, Introductory and Advanced Topics. Person education, 2006.

W. E. Spangler, J. H. May and L. G. Vargas, “Choosing Data-Mining Methods for Multiple Classification: Representational and Performance Measurement Implications for Decision Support,” Journal of Management Information Systems, vol. 16, no. 1, pp. 37-62, 2015.

P. Indyk and R. Motwani, “Approximate Nearest Neighbors: Towards Removing The Curse of Dimensionality,” in ACM Symposium on Theory of Computing, 1998.

D. G. Lowe, “Object Recognition From Local Scale-Invariant Features,” Computer Science Department University of British Columbia Vancouver, Canada, Proc. of the International Conference on Computer Vision, Corfu, Sept 1999.

Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li, “Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search,” in the proceedings of the 33rd International Conference on Very Large Data Bases, VLDB‘07, VLDB Endowment, pp. 950–961, 2007.

A. Gionis, P. Indyk, and R. Motwani, “Similarity Search in High Dimensions via Hashing,” In the Proceedings of the 25th International Conference on Very Large Data Bases, VLDB’99, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc, pp. 518–529, 1999.

W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, pp. 399-458, 2003.