Identification of students who copy in the classroom using Convolutional Neural Network
Abstract
The modernization of the educational process implies the automation of academic and administrative activities that promote an intelligent environment. Incorporating emerging technologies in higher-level educational institutions will make it possible to move towards the conversion of routine processes to improve the quality of the educational service. The present work consists of automating the detection of students who copy during the application of their exams in their classrooms using Deep Learning techniques with convolutional neural networks. An accuracy of 95.75% was obtained in the classification model after experimenting with different parameters and characteristics of a convolutional neural network.
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References
Delgado, A., Timana, D., Golondrino, E. and Villalba K., (2019). Arquitectura IoT para la identificación de personas en entornos educativos, Revista Ibérica de Sistemas y Tecnologías de Información, 841-853.
Krizhevsky, A, I Sutskever, y G Hinton (2012), Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, 1097-1105. DOI:10.1145/3065386
LeCun, Y. (2012). Learning Invariant Feature Hierarchies. European Conference on Computer Vision, European Conference on Computer Vision. DOI: 10.1007/978-3-642-33863-2_51
LeCun, Y, Y Bengio, y G Hinton. (2015). Deep learning. Nature. 521(7553), 436-444.
Sudhakar, S., Saberian, M. y Li, J. (2015). Multi-view face detection using Deep Convolutional Neural Networks. Sitio oficial de la Association for Computing Machinery. DOI: 10.1145/2671188.2749408
Szegedy, C. (2015), Going deeper with convolutions, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. DOI:10.1109/CVPR.2015.7298594
Triantafyllidou, D. y Tefas, A. (2016) Face detection based on deep convolutional neural networks exploiting incremental facial part learning, 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.,3560-3565. DOI:10.1109/ICPR.2016.7900186
Vázquez, M. (2014) Sistema de reconocimiento facial mediante técnicas de visión tridimensional [Tesis de maestría. Centro de investigaciones en Óptica]. Repositorio Institucional.
Zeiler, M D, y R Fergus. Visualizing and understanding convolutional networks. Lecture Notes in Bioinformatics, 2014. DOI:10.1007/978-3-319-10590-1_53
