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

Long, Duong Thang

doi:10.5815/ijmecs.2020.06.02

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Prior research has utilized biometrics for identification and monitoring during online learning and testing sessions [9][10][11], yet a system for face recognition and detecting facial expressions would be more user-friendly, considering that most computers or user devices come with built-in cameras for online learning. Moreover, the FR/FER system can consistently identify learners throughout their learning journey or real-time tracking of students' learning interactions within the learning management system (LMS), enabling educators and administrators to more effectively address each learner's training process [4,12,13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-Task CNN for Face and Facial Expression Recognition Using Residual and Dense Architectures for Application in Monitoring Online Learning

Long

2023

IJFIS

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Multi-task convolutional neural networks (MTCNN) have garnered substantial attention for their impressive feats in image classification. However, MTCNNs tailored for the dual tasks of face recognition and facial expression recognition remain relatively scarce. These networks often entail complexity and substantial computational demands, rendering them unsuitable for practical systems constrained by computational limitations. Addressing this gap, the present paper proposes an efficient MTCNN model that capitalizes on cutting-edge architecturesresidual networks and dense-connected networks-to adeptly execute face recognition and facial expression recognition tasks. The model's efficacy is rigorously assessed across wellknown datasets (JAFFE, CK+, OuluCASIA, KDEF) as well as collected images from learners (HOUS22). The proposed model consistently attains high accuracy levels across all datasets, with a notable minimum accuracy of 99.55% on testing data. These outcomes stand as a testament to the model's remarkable performance, particularly in relation to its streamlined design. Moreover, the model is seamlessly integrated with an online learning management system, furnishing a versatile means of monitoring learning activities to enhance the overall training quality.

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Section: Introductionmentioning

confidence: 99%

Efficient Multi-Task CNN for Face and Facial Expression Recognition Using Residual and Dense Architectures for Application in Monitoring Online Learning

Long

2023

IJFIS

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Designing Intelligent Multi-agent Ontology-Based Training Systems: The Case of State University of Infrastructure and Technology

Tkachenko

2022

Lecture Notes in Networks and Systems

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Mobile-YOLO: A Lightweight and Efficient Implementation of Object Detector Based on YOLOv4

Wang

Tong

Gao

et al. 2022

Advances in Computer Science for Engineering and Education

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

Cited by 5 publications

References 11 publications

Efficient Multi-Task CNN for Face and Facial Expression Recognition Using Residual and Dense Architectures for Application in Monitoring Online Learning

Efficient Multi-Task CNN for Face and Facial Expression Recognition Using Residual and Dense Architectures for Application in Monitoring Online Learning

Designing Intelligent Multi-agent Ontology-Based Training Systems: The Case of State University of Infrastructure and Technology

Mobile-YOLO: A Lightweight and Efficient Implementation of Object Detector Based on YOLOv4

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