Facial Sentiment Recognition using artificial intelligence techniques.
DOI:
https://doi.org/10.4108/eetcasa.v9i1.3930Keywords:
Facial Sentiment Recognition, Convolutional artificial neural network, Linear regression, Satisfied predictionAbstract
Facial emotion recognition technology is used to analyze and recognize human emotions based on facial expressions. This
technology uses deep learning models to classify facial expressions, eyes, eyebrows, mouth, and other facial expressions to
determine a person's emotions. The application of facial emotion recognition in the field of education is a potential way to
evaluate the level of student absorption after each class period. Using cameras and emotion recognition technology, the
system can record and analyze students' facial expressions during class. In this paper, we use the Convolutional Neural
Network (CNN) algorithm combined with the linear regression analysis method to build a model to predict students' facial
emotions over a period of time camera recorded.
References
Matsumoto, David, and Hyi Sung Hwang. Reading facial expressions of emotion, Psychological Science, 2011. DOI: https://doi.org/10.1037/e574212011-002
D. Yanga, Abeer Alsadoona, P.W.C. Prasad, A. K. Singhb, A. Elchouemic, An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment, Elsevier B.V, 2018 DOI: https://doi.org/10.1016/j.procs.2017.12.003
K. Mase, A. Pentland. Recognition of facial expression from optical flow, IEEE TRANSACTIONS on Information and Systems, Vol E74-D, No10, pp. 3474-3483, 1991
I Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B Hamner, W Cukierski, Y Tang, DH Lee, Y Zhou, C Ramaiah, F Feng, R Li, X Wang, D Athanasakis, J Shawe-Taylor, M Milakov, J Park, R Ionescu, M Popescu, C Grozea, J Bergstra, J Xie, L Romaszko, B Xu, Z Chuang, and Y. Bengio. Challenges in Representation Learning: A report on three machine learning contests. arXiv 2013, 2013. DOI: https://doi.org/10.1007/978-3-642-42051-1_16
Bengio, Yoshua. Learning Deep Architectures for AI, Foundations and Trends in Machine Learning: Vol. 2: No.1, pp 1-127, 2009. DOI: https://doi.org/10.1561/2200000006
Paul Viola and Michael Jones. Rapid Object Detection using a Boosted Cascade of Simple Features IEEE, 2001.
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, ICML, 2009.
Soad Almabdy, Lamiaa Elrefaei, Deep convolutional neural network-based approaches for face recognition, Appl. Sci, Doi:10.3390/app9204397, 2019. DOI: https://doi.org/10.3390/app9204397
Keyur Patel, Dev Mehta, Chinmay Mistry, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Mamoun Alazab. Facial Sentiment Analysis using AI Techniques: State-of-the-Art, Taxonomies, and Challenges, IEEE Access, Doi 10.1109/ACCESS.2020.2993803, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2993803
Wafa Mellouk, Wahida Handouzi. Facial emotion recognition using deep learning: review and insights, Elsevier B.V, 2020. DOI: https://doi.org/10.1016/j.procs.2020.07.101
C. F. Benitez-Quiroz, R. Srinivasan, and A. M. Martinez. Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild, in Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016. DOI: https://doi.org/10.1109/CVPR.2016.600
Imane Lasri, Anouar Riad Solh, Mourad El Belkacemi. Facial Emotion Recognition of Students using Convolutional Neural Network, IEEE, 2019. DOI: https://doi.org/10.1109/ICDS47004.2019.8942386
A. Mollahosseini, B. Hasani, and M. H. Mahoor. Affectnet: A database for facial expression, valence, and arousal computing in the wild, IEEE Transactions on Affective Computing, vol. PP, no. 99, pp. 1-1, 2017.
X. Liu, B. Kumar, J. You, and P. Jia, Adaptive deep metric learning for identity-aware facial expression recognition, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, pp. 522-531, 2017. DOI: https://doi.org/10.1109/CVPRW.2017.79
V. Vielzeuf, S. Pateux, and F. Jurie, Temporal multimodal fusion for video emotion classification in the wild, Proc. ACM International Conference on Multimodal Interaction, pp. 569-576, 2017. DOI: https://doi.org/10.1145/3136755.3143011
Vladimir Cherkassky, Yunqian Ma. Selecting the Loss Function for Robust Linear Regression, NC 2569 Under Review in Neural Computation, Revised June 10, 2002
Xiaogang Su, Xin Yan, Chih-Ling Tsai. Linear regression, Volume 4, May/June 2012 Wiley Periodical s, Inc, https://doi.org/10.1002/wics.1198, 2012. DOI: https://doi.org/10.1002/wics.1198
Shuang Liu, Dahua Li, Qiang Gao, Yu Gong, Facial Emotion Recognition Based on CNN, IEEE, 2020.
S. Turabzadeh, H. Meng, R. Swash, M. Pleva, and J. Juhar, Facial Expression Emotion Detection for Real-Time Embedded Systems, Technologies, vol. 6, no. 1, p. 17, Jan, 2018. DOI: https://doi.org/10.3390/technologies6010017
J. Flores. Training a TensorFlow model to recognize emotions, Available: https://medium.com/@jsflo.dev/training-a-tensorflow-model-to-recognize-emotions-a20c3bcd6468, 2018
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