Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hui Yu
Keyword(s):  
Data Mining ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Practical Significance ◽  
Expression Recognition

The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.

scholarly journals eXnet: An Efficient Approach for Emotion Recognition in the Wild

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1087
Author(s):  
Muhammad Naveed Riaz ◽  
Yao Shen ◽  
Muhammad Sohail ◽  
Minyi Guo
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Data Augmentation ◽  
Raspberry Pi ◽  
Expression Recognition ◽  
Extensive Evaluation ◽  
Benchmark Datasets ◽  
Augmentation Techniques ◽  
In The Wild

Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for real-time systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.

scholarly journals Are affective factors related to individual differences in facial expression recognition?

2020 ◽  
Vol 7 (9) ◽  
pp. 190699
Author(s):  
Sarah A. H. Alharbi ◽  
Katherine Button ◽  
Lingshan Zhang ◽  
Kieran J. O'Shea ◽  
Vanessa Fasolt ◽  
...  
Keyword(s):  
Individual Differences ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Expression Recognition ◽  
General Anxiety ◽  
Affective Factors ◽  
Multimodal Emotion Recognition ◽  
Anxiety Subscale

Evidence that affective factors (e.g. anxiety, depression, affect) are significantly related to individual differences in emotion recognition is mixed. Palermo et al . (Palermo et al . 2018 J. Exp. Psychol. Hum. Percept. Perform. 44 , 503–517) reported that individuals who scored lower in anxiety performed significantly better on two measures of facial-expression recognition (emotion-matching and emotion-labelling tasks), but not a third measure (the multimodal emotion recognition test). By contrast, facial-expression recognition was not significantly correlated with measures of depression, positive or negative affect, empathy, or autistic-like traits. Because the range of affective factors considered in this study and its use of multiple expression-recognition tasks mean that it is a relatively comprehensive investigation of the role of affective factors in facial expression recognition, we carried out a direct replication. In common with Palermo et al . (Palermo et al . 2018 J. Exp. Psychol. Hum. Percept. Perform. 44 , 503–517), scores on the DASS anxiety subscale negatively predicted performance on the emotion recognition tasks across multiple analyses, although these correlations were only consistently significant for performance on the emotion-labelling task. However, and by contrast with Palermo et al . (Palermo et al . 2018 J. Exp. Psychol. Hum. Percept. Perform. 44 , 503–517), other affective factors (e.g. those related to empathy) often also significantly predicted emotion-recognition performance. Collectively, these results support the proposal that affective factors predict individual differences in emotion recognition, but that these correlations are not necessarily specific to measures of general anxiety, such as the DASS anxiety subscale.

scholarly journals Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luxin Jiang ◽  
Xiaohui Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Particle Swarm ◽  
Teaching Quality ◽  
Particle Swarm Algorithm ◽  
Bp Model ◽  
Quality Evaluation Model

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

Real-time facial expression recognition using smoothed deep neural network ensemble

2020 ◽  
Vol 28 (1) ◽  
pp. 97-111
Author(s):  
Nadir Kamel Benamara ◽  
Mikel Val-Calvo ◽  
Jose Ramón Álvarez-Sánchez ◽  
Alejandro Díaz-Morcillo ◽  
Jose Manuel Ferrández-Vicente ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Facial Emotion Recognition ◽  
Training Data ◽  
Facial Emotion ◽  
Processing Unit ◽  
Expression Recognition ◽  
Neural Network Ensemble

Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.

Intelligent online teaching system based on SVM algorithm and complex network

2020 ◽  
pp. 1-11
Author(s):  
Chuanxin Fang
Keyword(s):  
Online Teaching ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Research Work ◽  
Principal Component ◽  
Teaching Quality ◽  
Support Vector ◽  
School Teaching ◽  
The Impact ◽  
Quality Evaluation Model

English Online teaching quality evaluation refers to the process of using effective technical means to comprehensively collect, sort and analyze the teaching status and make value judgments to improve teaching activities and improve teaching quality. The research work of this paper is mainly around the design of teaching quality evaluation model based on machine learning theory and has done in-depth research on the preprocessing of evaluation indicators and the construction of support vector machine teaching quality evaluation model. Moreover, this study uses improved principal component analysis to reduce the dimensionality of the evaluation index, thus avoiding the impact of the overly complicated network model on the prediction effect. In addition, in order to verify that the model proposed in this study has more advantages in evaluating teaching quality than other shallow models, the parameters of the model are tuned, and a control experiment is designed to verify the performance of the model. The research results show that this research model has a certain effect on the evaluation of school teaching quality, and it can be applied to practice.

scholarly journals Facial Expression Recognition Using Computer Vision: A Systematic Review

2019 ◽  
Vol 9 (21) ◽  
pp. 4678 ◽  
Author(s):  
Daniel Canedo ◽  
António J. R. Neves
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Future Research ◽  
Expression Recognition ◽  
Contemporary World

Emotion recognition has attracted major attention in numerous fields because of its relevant applications in the contemporary world: marketing, psychology, surveillance, and entertainment are some examples. It is possible to recognize an emotion through several ways; however, this paper focuses on facial expressions, presenting a systematic review on the matter. In addition, 112 papers published in ACM, IEEE, BASE and Springer between January 2006 and April 2019 regarding this topic were extensively reviewed. Their most used methods and algorithms will be firstly introduced and summarized for a better understanding, such as face detection, smoothing, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Optical Flow (OF), Gabor filters, among others. This review identified a clear difficulty in translating the high facial expression recognition (FER) accuracy in controlled environments to uncontrolled and pose-variant environments. The future efforts in the FER field should be put into multimodal systems that are robust enough to face the adversities of real world scenarios. A thorough analysis on the research done on FER in Computer Vision based on the selected papers is presented. This review aims to not only become a reference for future research on emotion recognition, but also to provide an overview of the work done in this topic for potential readers.

scholarly journals Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses

10.2196/13810 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e13810 ◽  
Author(s):  
Anish Nag ◽  
Nick Haber ◽  
Catalin Voss ◽  
Serena Tamura ◽  
Jena Daniels ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Eye Gaze ◽  
Recognition Task ◽  
Children With Autism ◽  
Expression Recognition ◽  
Eye Tracker ◽  
Gaze Tracking ◽  
Smart Glasses

Background Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings. Objective This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs). Methods We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision–enabled methods for pupil tracking and world–gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants. Results Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects. Conclusions Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype.

Facial Emotion Recognition Using an Ensemble of Multi-Level Convolutional Neural Networks

2019 ◽  
Vol 33 (11) ◽  
pp. 1940015 ◽  
Author(s):  
Hai-Duong Nguyen ◽  
Soonja Yeom ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang ◽  
In-Seop Na ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Research Area ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Network Connections ◽  
Multi Level ◽  
Machine Interaction ◽  
Made In

Emotion recognition plays an indispensable role in human–machine interaction system. The process includes finding interesting facial regions in images and classifying them into one of seven classes: angry, disgust, fear, happy, neutral, sad, and surprise. Although many breakthroughs have been made in image classification, especially in facial expression recognition, this research area is still challenging in terms of wild sampling environment. In this paper, we used multi-level features in a convolutional neural network for facial expression recognition. Based on our observations, we introduced various network connections to improve the classification task. By combining the proposed network connections, our method achieved competitive results compared to state-of-the-art methods on the FER2013 dataset.

An Evaluation Model of Polytechnic Teaching Quality Based on ID3 Decision Making Tree

2014 ◽  
Vol 651-653 ◽  
pp. 2437-2440 ◽  
Author(s):  
Wu Xue Jiang ◽  
Yin Zhen Zhong ◽  
Huan Liang
Keyword(s):  
Decision Tree ◽  
Quality Evaluation ◽  
Teaching Practice ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Practical Significance ◽  
Research Progress ◽  
Apriori Algorithm ◽  
High Credibility

For improving the quality of vocational education evaluation validity, this paper introduces the research progress of teaching evaluation, use data mining technology to put forward a feasible model of higher vocational teaching quality evaluation, construct a decision tree has practical significance. By the method used, the traditional Apriori algorithm is improved decision tree to improve the teaching contents to the location of the second master node, reflecting the characteristics of vocational teaching practice. Case analysis indicated, the decision tree has a good operability and high credibility.

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