A Facial Expression Recognition Model using Lightweight Dense-Connectivity Neural Networks for Monitoring Online Learning Activities

Long, Duong Thang; Tung, Truong Tien; Dung, Tran Tien

doi:10.5815/ijmecs.2022.06.05

Cited by 8 publications

(8 citation statements)

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“…In the neural network model [22][23][24], the structure of initial data matrix is 38 × 55. In the parameter setting, the raw data of 55 employees is used as input values.…”

Section: Weight Calculation Based On Bp Neural Networkmentioning

confidence: 99%

Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Hao,

Liu,

Tang

2024

Advances in Transdisciplinary Engineering

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Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a “3+X” talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China’s hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.

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“…In the neural network model [22][23][24], the structure of initial data matrix is 38 × 55. In the parameter setting, the raw data of 55 employees is used as input values.…”

Section: Weight Calculation Based On Bp Neural Networkmentioning

confidence: 99%

Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Hao,

Liu,

Tang

2024

Advances in Transdisciplinary Engineering

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“…For the purpose of fortifying the model's resilience and curbing overfitting during training, 2D image processing strategies are harnessed to augment the training images, aligning with recommendations from earlier studies [4,13]. This augmentation entails incorporating various techniques such as noise addition, rotation, flipping, cropping, shifting, and color adjustments, among others.…”

Section: Image Preprocessing and Augmentationmentioning

confidence: 99%

“…In practical scenarios, an amalgamation of the MTFace model and an LMS is designed to oversee and appraise online learning undertakings. This integration was accomplished using realized through the utilization of application programming interfaces (APIs) bridging the two systems, as expounded upon in [4]. The MTFace model is introduced as a module that can be executed on client devices, such as web clients or mobile applications.…”

Section: Design Applied Mtface Model To Lmsmentioning

confidence: 99%

“…Face recognition (FR) and facial expression recognition (FER) pose significant challenges in the field of computer vision. Convolutional neural networks (CNN) have emerged as the go-to choice for addressing these challenges, with prominent models like VGGNet, GoogleNet, ResNet, SENet, and DenseNet attaining remarkable outcomes [1][2][3][4][5][6]. VGGNet presents a straightforward architecture, while ResNet incorporates residual blocks (depicted in Figure 1(a)) featuring shortcut connections that mitigate the vanishing gradient problem and empower the network to learn residual attributes [7].…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Comparative Study on the Development of Chinese and Foreign Textbooks in Nanomaterials and Technology

Ding

Wen

Zhou

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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A Facial Expression Recognition Model using Lightweight Dense-Connectivity Neural Networks for Monitoring Online Learning Activities

Cited by 8 publications

References 0 publications

Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

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

Comparative Study on the Development of Chinese and Foreign Textbooks in Nanomaterials and Technology

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