Acceso abierto

Design and implementation of a virtual teacher teaching system algorithm based on facial expression recognition in the era of big data

 y    | 17 jul 2023

Cite

Jamalian, M., Vahdat-Nejad, H., & Hajiabadi, H. (2022). Investigating the Impact of COVID-19 on Education by Social Network Mining. Search in Google Scholar

Burns, A., Danyluk, P., Kapoyannis, T., & Kendrick, A. (2020). Leading the Pandemic Practicum: One Teacher Education Response to the COVID-19 Crisis. Canadian Network for Innovation in Education, 2. Search in Google Scholar

Wingenbach, T. S. H., Ashwin, C., & Brosnan, M. (2017). Diminished sensitivity and specificity at recognizing facial emotional expressions of varying intensity underlie emotion-specific recognition deficits in autism spectrum disorders. Research in Autism Spectrum Disorders, 34, 52-61. Search in Google Scholar

Ploiz, T., & Fink, G. A. (2016). Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs. Pattern Recognition, 39(12), 2267-2280. Search in Google Scholar

Mollahosseini, A., Chan, D., & Mahoor, M. H. (2015). Going deeper in facial expression recognition using deep neural networks. Computer Science, 1-10. Search in Google Scholar

Valstar, M. F., Mehu, M., Jiang, B., et al. (2012). Meta-Analysis of the First Facial Expression Recognition Challenge. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4), 966-979. Search in Google Scholar

Timur, S. (2018). Examining Cognitive Structures of Prospective Preschool Teachers Concerning the Subject “Force and Motion.” Educational Sciences: Theory and Practice, 12(4), 3039-3049. Search in Google Scholar

Kim, Y. S., & Um, B. J. (2011). Recommender System Based on Click Stream Data Using Association Rule Mining. Expert Systems with Application, 8(8), 3320-13327. Search in Google Scholar

Butler, D. L. (2014). Collaboration and self-regulation in teachers’ professional development. Teaching and Teacher Education, 20(5), 435-455. Search in Google Scholar

Calabrese, R., & Russo, K. E. (2016). Sbateyl.org: A Virtual Space for Effective Language Training. Procedia - Social and Behavioral Sciences, 228. Search in Google Scholar

Micoulaud-Franchi, J.-A., Quiles, C., Fond, G., Cermolacce, M., & Vion-Dury, J. (2014). The covariation of independent and dependent variables in neurofeedback: A proposal framework to identify cognitive processes and brain activity variables. Consciousness and Cognition, 26. Search in Google Scholar

Guha, M. L., Druin, A., & Fails, J. A. (2013). Cooperative Inquiry revisited: Reflections of the past and guidelines for the future of intergenerational co-design. International Journal of Child-Computer Interaction, 1(1). Search in Google Scholar

Cakula, S., & Sedleniece, M. (2013). Development of a personalized e-learning model using methods of Ontology. Procedia Computer Science, 26, 113-120. Search in Google Scholar

Johnson, W. L., & Shaw, E. (2017). Using agents to overcome deficiencies in Web-based Courseware. In Proceedings of the workshop, Intelligent Educational Systems on the World Wide Web, 8th World Conference of the AIED Society, Kobe, Japan. Search in Google Scholar

Choi, C. S., Aizawa, K., Harashima, H., et al. (2014). Analysis and synthesis of facial image sequences in model-based image coding. IEEE Transactions on Circuits and Systems for Video Technology, 4(3), 257-275. Search in Google Scholar

Wen, Y., Zhang, K., Li, Z., et al. (2016). A Discriminative feature learning approach for deep face recognition. In Computer Vision–ECCV 2016. Springer International Publishing, 499-515. Search in Google Scholar

Russakovsky, O., Deng, J., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252. Search in Google Scholar

Shan, K., Guo, J., You, W., et al. (2017). Automatic facial expression recognition based on a deep convolutional-neural-network structure. In IEEE International Conference on Software Engineering Research, Management and Applications. IEEE, 123-128. Search in Google Scholar

Dornaika, F., Moujahid, A., Raducanu, B., et al. (2013). Facial expression recognition using tracked facial actions: Classifier performance analysis. Engineering Applications of Artificial Intelligence: The International Journal of Intelligent Real-Time Automation, 26(1), 467-477. Search in Google Scholar

Oh, Y.-H., Ngo, A. C. L., See, J., et al. (2015). Monogenic Riesz wavelet representation for micro-expression recognition. In 2015 IEEE International Conference on Digital Signal Processing (DSP 2015), Singapore, 1237-1241. Search in Google Scholar

Chaudilari, S., & Gulati, R. M. (2016). Script identification using Gabor feature and SVM classifier. Procedia Computer Science, 3, 107-112. Search in Google Scholar

Oh, S. K., Yoo, S.-H., Pedrycz, W., et al. (2013). Design of face recognition algorithm using PCA-LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application. Expert Systems with Applications, 40(5), 1451-1466. Search in Google Scholar

Mutelo, R., Woo, W., & Dlay, S. (2018). Discriminant analysis of the two-dimensional Gabor features for face recognition. IET Computer Vision, 2(2), 37-49. Search in Google Scholar

Shu, C., Ding, X., & Fang, C. (2018). Histogram of the Oriented Gradient for Face Recognition. Tsinghua Science and Technology, 16(2), 216-224. Search in Google Scholar

Sagonas, C., Antonakos, E., Tzimiropoulos, G., et al. (2016). 300 Faces In-The-Wild Challenge: database and results. Image & Vision Computing, 47, 3-18. Search in Google Scholar

Han, Y. Z., Cheng, K. Y., Chen, Y. B., et al. (2015). A new classifier for facial expression recognition: Fuzzy buried Markov model. Journal of Computer Science and Technology, 25(3), 641–650. Search in Google Scholar

Kuyuk, H. S., Yildirim, E., Dogan, E., et al. (2014). Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis. Journal of Earth Science, 25(1), 140–145. Search in Google Scholar

Lienhart, R., & Wernicke, A. (2010). Localizing and segmenting text in images and videos. IEEE Transactions on Circuits & Systems for Video Technology, 12(4), 256-268. Search in Google Scholar

Ojala, T., Pietikäinen, M., & Mäenpää, T. (2017). Gray scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24(7), 971-987. Search in Google Scholar

Chang, C.-C., & Lin, C.-J. (2017). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems & Technology, 2(3), 389-396. Search in Google Scholar

Jabid, T., Kabir, M. H., & Chae, O. (2019). Robust facial expression recognition based on local directional pattern. ETRI Journal, 32(5), 784-794. Search in Google Scholar

eISSN:
2444-8656
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics