Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System

Fakhar, None Shariqa; Baber, Junaid; Bazai, Sibghat Ullah; Marjan, Shah; Jasiński, Michał; Jasińska, Elżbieta; Chaudhry, Muhammad Umar; Leonowicz, Zbigniew; Hussain, Shumaila

doi:10.3390/app122312134

Cited by 13 publications

(5 citation statements)

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“…The specific requirements in the field of face identification and facial emotion recognition have been solved with different types of neural network architectures. For instance, pre-trained networks can be used for the following tasks: Classification, which can apply pre-trained networks directly to classification tasks [ 34 , 35 , 38 , 53 , 80 ]. Feature extraction, which is pre-trained network which can be used as a feature extractor using the activation layers as features, and these layers can be used to train other machine learning models, such as a support vector machine (SVM) [ 62 , 77 , 83 , 90 , 101 ].…”

Section: New Trends In Using Neural Network For Fermentioning

confidence: 99%

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New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

Cîrneanu,

Popescu,

Iordache

2023

Sensors

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Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper’s scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.

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Section: New Trends In Using Neural Network For Fermentioning

confidence: 99%

“…Classification, which can apply pre-trained networks directly to classification tasks [ 34 , 35 , 38 , 53 , 80 ].…”

Section: New Trends In Using Neural Network For Fermentioning

confidence: 99%

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

Cîrneanu,

Popescu,

Iordache

2023

Sensors

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“…In addition to the above network models, some hybrid network models have also been proposed. These models can be divided into deep learning-based multinetwork hybrid models [55], [59] and manual-featureand-depth-feature-combination hybrid networks [1], [56], [58], [84]. One study [55] proposed a model for continuous facial emotional pattern recognition that combines a CNN, LSTM, and facial emotion recognition.…”

Section: ) Hybrid Network Modelmentioning

confidence: 99%

Facial Expression Recognition in Educational Research From the Perspective of Machine Learning: A Systematic Review

Fang,

Li,

Han

et al. 2023

IEEE Access

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Facial expression analysis aims to understand human emotions by analyzing visual face information and is a popular topic in the computer vision community. In educational research, the analyzed students' aect states can be used by faculty members as feedback to improve their teaching style and strategy so that the learning rate of all the students present can be enhanced. Facial expression analysis has attracted much attention in educational research, and a few reviews on this topic have emerged. However, previous reviews on facial expression recognition methods in educational research focus mostly on summarizing the existing literature on emotion models from a theoretical perspective, neglecting technical summaries of facial expression recognition. In order to advance the development of facial expression analysis in educational research, this paper outlines the tasks, progress, challenges, and future trends related to facial expression analysis. First, facial expression recognition methods in educational research lack an overall framework. Second, studies based on the latest machine learning methods are not mentioned in previous reviews. Finally, some key challenges have not been fully explored. Therefore, unlike previous reviews, this systematic review summarizes two kinds of educational research methods based on facial expression recognition and their application scenarios. Then, an overall framework is proposed, along with various kinds of machine learning methods and published datasets. Finally, the key challenges of face occlusion and the expression uncertainty problem are presented. This study aims to capture the full picture of facial expression recognition methods in educational research from a machine learning perspective.

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“…This can cultivate students' thinking leaps, better open up their thinking and knowledge, and cultivate their autonomous learning ability. The importance of basic English curriculum education is already self-evident, and it is of great significance for the cultivation of versatile talents and the future talent planning of the country [11]. Especially in the context of the rise of smart classrooms, the reform of basic English curriculum education has encountered unprecedented development opportunities.…”

Section: Introductionmentioning

confidence: 99%

An ExpTODIM-MACONT Based Multiple-Attribute Group Decision-Making Technique for Smart Classroom Teaching Evaluation of Basic English Under Interval-Valued Pythagorean Fuzzy Circumstances

Liang

2024

IEEE Access

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With the rapid development of information technology, the university teaching model has also begun to break free from constraints from two dimensions of time and space, becoming a new teaching model -smart classroom. Supported by the theory of smart classrooms, the reform of basic English curriculum teaching in China has received much attention. Currently, the reform of basic English curriculum teaching mainly faces three problems: incomplete resource allocation, limited development conditions, and poor practicality. Based on this, utilizing the theory of smart classrooms, we can solve the problem of incomplete resource allocation by building an "educational community", innovate teaching models, enhance students' English autonomous learning and practical application abilities, and achieve strategic analysis of basic English curriculum teaching reform. The smart classroom teaching evaluation of basic English is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM (an acronym in Portuguese of interactive and multicriteria decision making) technique and Mixed Aggregation by Comprehensive Normalization Technique (MACONT) has been used to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are used as a tool for characterizing uncertain information during the smart classroom teaching evaluation of basic English. In this manuscript, the interval-valued Pythagorean fuzzy Exponential TODIM-MACONT (IVPF-ExpTODIM-MACONT) technique is built to solve the MAGDM under IVPFSs. In the end, a numerical case study for smart classroom teaching evaluation of basic English is implemented to validate the proposed technique. The main contributions of this paper are outlined: ( 1) the ExpTODIM and MACONT technique has been extended to IVPFSs; (2) Information Entropy technique is employed to manage the weight values under IVPFSs. (3) the IVPF-ExpTODIM-MACONT technique is founded to implement the MAGDM under IVPFSs; (4) a numerical case study for smart classroom teaching evaluation of basic English and some comparative analysis is supplied to verify the IVPF-ExpTODIM-MACONT technique. Thus, the main advantages of the proposed IVPF-ExpTODIM-MACONT technique are outlined: (1) the proposed IVPF-ExpTODIM-MACONT not only handles the uncertainty in real MADM issues, but also portrays the DMs' psychological behavior during the smart classroom teaching evaluation of basic English. (2) the proposed IVPF-ExpTODIM-MACONT analyze the behavior of the TODIM and MACONT as MADM techniques when they are hybridized. INDEX TERMS: Multiple attribute group decision making (MAGDM); interval-valued Pythagorean fuzzy sets (IVPFSs); Exponential TODIM (ExpTODIM) technique; MACONT technique; smart classroom teaching evaluation

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Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System

Cited by 13 publications

References 79 publications

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

Facial Expression Recognition in Educational Research From the Perspective of Machine Learning: A Systematic Review

An ExpTODIM-MACONT Based Multiple-Attribute Group Decision-Making Technique for Smart Classroom Teaching Evaluation of Basic English Under Interval-Valued Pythagorean Fuzzy Circumstances

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