International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.12, No.6, Dec. 2020

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

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Duong Thang Long

Index Terms

Convolutional neural networks, face recognition, online student monitoring


Using convolution neural network (CNN) for face recognition is being widely research with a promising significant in applications and it is interested by many authors. Moreover, the CNN model has brought successful applications in practice such as detection and identification face of people on Facebook users' photos application, they use DeepFace model. There are many articles which proposed CNN models for face recognition with using some modifications of popular models of large architectures such as VGG, ResNet, OpenFace or FaceNet. However, these models are large complexity for some applications in reality with limitations of computing resources. This paper proposes a design of CNN model with moderate complexity but still ensures the quality and efficiency of face recognition. We run experiments for evaluating the model on some popular datasets, the experiment shows effective results and indicates that the proposed model can be practically used. 

Cite This Paper

Duong Thang Long, " A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.6, pp. 16-28, 2020.DOI: 10.5815/ijmecs.2020.06.02


[1]M. A. Abuzneid and A. Mahmood, “Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and BPNN”, IEEE Access, Vol. 6, pp.20641-20651, 2018.

[2]Brandon Amos et al., “OpenFace: A general-purpose face recognition library with mobile applications”, CMU School of Computer Science, Tech. Rep., 2016.

[3]Shraddha Arya and Arpit Agrawal, “Face Recognition with Partial Face Recognition and Convolutional Neural Network”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol.7, Iss.1, pp.91-94, ISSN: 2278 – 1323, 2018.

[4]Qiong Cao et al., “VGGFace2 - A dataset for recognising faces across pose and age”, IEEE Conference on Automatic Face and Gesture Recognition ( ∼vgg/data/vgg face2), 2018.

[5]Lionel Landry S. Deffo et al., “CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition”, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 12, pp.240-244, 2018.

[6]Ekberjan Derman and Albert Ali Salah, “Continuous Real-Time Vehicle Driver Authentication Using Convolutional Neural Network Based Face Recognition”, 13th IEEE International Conference on Automatic Face & Gesture Recognition, 2018.

[7]Ayham Fayyoumi and Anis Zarrad, “Novel Solution Based on Face Recognition to Address Identity Theft and Cheating in Online Examination Systems”, Advances in Internet of Things, Vol.4, pp.5-12, 2014.

[8]Chunrui Han et al., “Face Recognition with Contrastive Convolution”, European Conference on Computer Vision: Computer Vision – ECCV, pp.120-135, 2018.

[9]Patrik Kamencay et al., “A New Method for Face Recognition Using Convolutional Neural Network”, Digital Image Processing and Computer Graphics, Vol. 15, No. 4, pp.663-672, 2017.

[10]Hoda Mohammadzade et al., “Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches”, Journal of the Optical Society of America, A.35(7), 2018.

[11]Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, University of Oxford, 2015.

[12]James Philbin et al., “FaceNet: A Unified Embedding for Face Recognition and Clustering”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[13]Kevin Santoso et al., “Face Recognition Using Modified OpenFace”, 3rd International Conference on Computer Science and Computational Intelligence, Procedia Computer Science, No.135, pp.510–517, 2018.

[14]R. Syafeeza et al., “Convolutional Neural Network for Face Recognition with Pose and Illumination Variation”, International Journal of Engineering and Technology (IJET), pp.44-57, 2014.

[15]Muhtahir O. Oloyede et al., “Improving Face Recognition Systems Using a New Image Enhancement Technique, Hybrid Features and the Convolutional Neural Network”, IEEE Access, Vol. 6, pp. 75181-75191, 2018.

[16]Ramprasaath R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization”, IEEE International Conference on Computer Vision (ICCV), Electronic ISSN: 2380-7504, 2017.

[17]Mei Wang et al., Deep Face Recognition: A Survey, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China, 2019.

[18]Rikiya Yamashita et al., Convolutional neural networks: an overview and application in radiology, Insights into Imaging, vol.9, pp.611–629, 2018.

[19]Md Zahangir Alom et al., A State-of-the-Art Survey on Deep Learning Theory and Architectures, Electronics, vol.8, no.292, 2019.

[20]Diederik P. Kingma et al., Adam: A Method for Stochastic Optimization, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. 2015.

[21]Umara Zafar et al., Face recognition with Bayesian convolutional networks for robust surveillance systems, EURASIP Journal on Image and Video Processing 2019:10.

[22]Ni Kadek Ayu Wirdiani, Real-Time Face Recognition with Eigenface Method, I.J. Image, Graphics and Signal Processing, vol.11, pp.1-9, 2019.

[23]Rafflesia Khan and Rameswar Debnath, Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence, I.J. Image, Graphics and Signal Processing, vol.2, pp.19-29, 2020.

[24]Zoran Kotevski et al., On the Technologies and Systems for Student Attendance Tracking, I.J. Information Technology and Computer Science, vol.10, pp.44-52, 2018.