Document Type : Research Article


1 Mathematics & Computer Department, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco.

2 LISTA Laboratory Faculty of sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah Unversity, Fs, Morocco.


Because face can reveal so much hidden information, we need to interpret these data and benefit from them. Hence, our paper shows a new and productive facial image representation based on local sensitive hashing (LSH). This strategy makes it conceivable to recognize the students who pursue their preparation in our learning training; during every session, an image of the learner will be taken by the webcam to be compared to that already stored in the database. As soon as the learner is recognized, he/she must be arranged in the accordion to an appropriate profile that takes into consideration his/her weaknesses and strength, which is conducted with the help of the J48 as a predictive study. Furthermore, we utilize a light processing module on the client device with a compact code in order that we can have a lot of in formation transmission capable to send the component over the network and to have the option to record many photos in an enormous database in the cloud.


[1] Xiaohu Ge, Xi Huang, Yuming Wang, Min Chen, Qiang Li, Tao Han, and Cheng-Xiang Wang. Energy-efficiency optimization for mimo-ofdm mobile multimedia communication systems with qos constraints. IEEE Transactions on Vehicular Technology, 63(5):2127–2138, 2014.
[2] Matthew Turk and Alex Pentland. Eigenfaces for recognition. Journal of cognitive neuroscience, 3 (1):71–86, 1991.
[3] Kamran Etemad and Rama Chellappa. Discriminant analysis for recognition of human face images. Josa a, 14(8):1724–1733, 1997.
[4] Jian Yang, David Zhang, Alejandro F Frangi, and Jing-yu Yang. Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE transactions on pattern analysis and machine intelligence, 26(1):131–137, 2004.
[5] Josef Sivic, Mark Everingham, and Andrew Zisserman. who are you?-learning person specific classifiers from video. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 1145–1152. IEEE, 2009.
[6] Jianqiu Ji, Shuicheng Yan, Jianmin Li, Guangyu Gao, Qi Tian, and Bo Zhang. Batch-orthogonal locality-sensitive hashing for angular similarity. IEEE transactions on pattern analysis and machine intelligence, 36(10):1963–1974, 2014.
[7] Xiao-Tong Yuan, Xiaobai Liu, and Shuicheng Yan. Visual classification with multitask joint sparse representation. IEEE Transactions on Image Processing, 21(10):4349–4360, 2012.
[8] B Kavitha, S Karthikeyan, and B Chitra. Efficient intrusion detection with reduced dimension using data mining classification methods and their performance comparison. In International Conference on Business Administration and Information Processing, pages 96–101. Springer, 2010.
[9] H Dunham Margaret. Data mining introductory and advanced topics. Pearsons Education Inc, 2003.
[10] William E Spangler, Jerrold H May, and Luis G Vargas. Choosing data-mining methods for multiple classification: representational and performance measurement implications for decision support. Journal of Management Information Systems, 16(1):37–62, 1999.
[11] Piotr Indyk and Rajeev Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In Proceedings of the thirtieth annual ACM symposium on Theory of computing, pages 604–613, 1998.
[12] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150–1157. Ieee, 1999.
[13] Qin Lv, William Josephson, Zhe Wang, Moses Charikar, and Kai Li. Multi-probe lsh: efficient indexing for high-dimensional similarity search. In 33rd International Conference on Very Large Data Bases, VLDB 2007, pages 950–961. Association for Computing Machinery, Inc, 2007.
[14] Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al. Similarity search in high dimensions via hashing. In Vldb, volume 99, pages 518–529, 1999.
[15] Wenyi Zhao, Rama Chellappa, P Jonathon Phillips, and Azriel Rosenfeld. Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4):399–458, 2003.