Classroom Teaching Evaluation Based on Facial Expression Recognition

Tang, Xiaoyu; Peng, Wang-yue; Liu, Sirui; Xiong, Jianwen

doi:10.1145/3383923.3383949

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Based on the selected reviewed publications, it was found that El Hammoumi et al [31], Ma et al [32], Hung et al [39], Lasri et al [40], Tang et al [41], Shi et al [42], Dash et al [45], Bian et al [46], Tang et al [51], Wang et al [7], Pise et al [56], Hingu [62], and Zakka and Vadapalli [63] have applied CNN for the feature extraction.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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Facial emotion expressions are among the most potent, natural, and powerful means of human communication. Due to the COVID-19 pandemic, educational institutions worldwide are forced to switch rapidly to remote and online learning. Students are currently in an emergency state and must adapt to various and readily accessible learning methods, such as mobile learning applications or an e-learning system. A systematic literature review (SLR) is conducted to extract and synthesize information such as the emotion classifier used in the facial expression recognition (FER) system, the dataset used, the preprocessing technique applied, the feature extraction approach used, and the strength and limitation of the previous studies. Based on the search criteria, 701 publications were initially retrieved from five different digital databases, of which 48 studies have been chosen as primary studies for further analysis. Based on the findings of this study, the deep learning approach is the most frequently adopted approach in classifying student emotions during online learning. FER-2013 is the most commonly used FER dataset in FER studies, while DAiSEE is the most used academic emotion dataset. Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. Next, it was found that the number of real-time FER systems is less than that of non-real-time FER systems. Finally, the top-1 accuracy of 94.6% was achieved by the long-term recurrent convolutional network on the academic emotion dataset, and the limitation is that it has low illumination and a lack of frontal pose.

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Section: Preprocessing Methodsmentioning

confidence: 99%

Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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“…There are a many studies in Facial Action Coding System such as social robots [32,33], medical treatment [34], driver fatigue monitoring [35,36], psychological monitoring [37][38][39], facial nerve grading in medicine [40], facial image compression and synthetic facial animation [41], video indexing, robotics, and virtual reality [42]. In addition, there are other studies such as examining change in emotional states of university students while interacting with smart learning systems [43], determining facial features to gauage emotional states during interaction in smart learning system [44], use Microsoft cloudbased Facial Emotion Recognizer software [45,46] and researching effectiveness of this software [47], analysis of facial expressions in formal and distance education environment [48][49][50], application of smart teaching assessment model to determine students' emotions with facial analysis [51].…”

Section: Previous Workmentioning

confidence: 99%

Effect of distance education courses held in different environments on emotions of the instructor

Tonguç

2024

PLoS ONE

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In this study, the emotional states of the instructors who teach by distance education and the effect of the environment in which they give the lesson on their emotions were examined. Computer-aided "Facial Action Coding System" method was used to measure emotion values from facial images. Through the software developed by the researchers using the Microsoft Face Recognition API, 43292 facial images taken from five trainers during their training were analysed and seven basic emotions representing facial expressions were obtained numerically. As a result of the analysis, it was found that the emotions of the instructors that can be described as negative in the lessons held in the e-studio environment generally increased at the beginning of the lesson, decreased in the following minutes and increased again at the end of the lesson; On the other hand, it was determined that positive emotions decreased at the beginning of the lesson and increased later. In the home environment, while the emotions that can be described as negative at the beginning decreased, positive emotions increased. A significant difference was determined between home and e-studio environment in all emotions except anger. One of the emotions with a difference in value between the two environments is happiness, and it has been determined that happiness has higher values in the home environment. It has been determined that other emotions are experienced more in the e-studio environment. It is thought that the results of the study will contribute to the mental states of the instructors who teach through distance education and to the efficiency of distance education.

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“…In sales applications (Ijjina et al, 2020), customers' facial expressions are essential data for computers to determine whether they need a human sales assistant. In classroom teaching (Pabba & Kumar, 2022; Tang et al, 2020; Tonguç & Ozkara, 2020), intelligent systems collect students' facial expressions and analyse expressions such as boredom, confusion, focus, yawning, and fatigue using facial expression recognition models to determine students' current learning states. Based on this information, instructors can timely adjust teaching strategies, such as incorporating more interactive activities and adapting the pace of the class, to enhance teaching quality and improve students' learning experience.…”

Section: Introductionmentioning

confidence: 99%

A multi‐scale feature fusion convolutional neural network for facial expression recognition

Zhang,

Fu,

et al. 2023

Expert Systems

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This paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

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Classroom Teaching Evaluation Based on Facial Expression Recognition

Cited by 9 publications

References 4 publications

Academic Emotion Classification Using FER: A Systematic Review

Academic Emotion Classification Using FER: A Systematic Review

Effect of distance education courses held in different environments on emotions of the instructor

A multi‐scale feature fusion convolutional neural network for facial expression recognition

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