ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Facial Emotion Recognition Using Convolutional Neural Network


Affiliations
1 Department of Computer Engineering, Tribhuvan University, Nepal
     

   Subscribe/Renew Journal


Facial expressions play a significant role in social communication since they convey a lot of information about people, such as moods, emotions, and other things. Many researchers gained an optimal accuracy in most of the popular facial recognition datasets: CK+, JAFFE, IEV, but in FER2013 the best model accuracy is about 74%. This article purpose deep learning-based models to mitigate this issue. Three models based on AlexNet, VGG19, and ResNet50 are used to train with the dataset, and the very best model among them is further analyzed. The best model is trained using various optimizers and evaluated based on its training and testing accuracy, confusion matrix, ROC Curve. The finest model gained an accuracy of 91.89504% which is better than past state of art models by at least 17% accuracy.

Keywords

Facial Expression, Confusion Matrix, Emotion, Optimizer, Receiver Operating Characteristic Curve.
Subscription Login to verify subscription
User
Notifications
Font Size

  • B.C. Ko, “A Brief Review of Facial Emotion Recognition based on Visual Information”, Sensors, Vol. 18, No. 2, pp. 401-421,2018.

  • H.D. Nguyen, S. Yeom, G.S. Lee, H.J. Yang, I.S. Na and S.H. Kim, “Facial Emotion Recognition using an Ensemble of Multi-Level Convolutional Neural Networks”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 33, No. 11, 2019.

  • Y.K. Bhatti, A. Jamil, N. Nida, M.H. Yousaf, S. Viriri and S.A. Velastin, “Facial Expression Recognition of Instructor using Deep Features and Extreme Learning Machine”, Computational Intelligence and Neuroscience, Vol. 2021, No. 1-14, 2021.

  • Ben Niu, Zhenxing Gao and Bingbing Guo, “Facial Expression Recognition with LBP and ORB Features”, Computational Intelligence and Neuroscience, Vol. 2021, pp. 1-16, 2021.

  • J. Daihong, Hu Yuanzheng, D. Lei and P. Jin, “Facial Expression Recognition Based on Attention Mechanism”, Scientific Programming, Vol. 2021, pp. 1-18, 2021.

  • L. Zahara, P. Musa, E. Prasetyo Wibowo, I. Karim and S. Bahri Musa, “The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi”, Proceedings of 5th International Conference on Informatics and Computing,pp. 1-9, 2020.

  • A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Communications of the ACM, Vol. 25, No. 6, pp. 1097-1105, 2012.

  • Simonyan Karen and Zisserman Andrew, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of 3rd International Conference on Learning Representations, pp. 1-6, 2015.

  • K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

  • D.P. Kingma and J.L. Ba, “Adam: A Method for Stochastic Optimization”, Proceedings of 3rd International Conference on Learning Representations, pp. 145-156, 2015.

  • Matthew D. Zeiler, “Adadelta: An Adaptive Learning Rate Method”, Proceedings of 3rd International Conference on Learning Representations, pp. 171-178, 2012.

  • Yann Dauphin, “RMSProp and Equilibrated Adaptive Learning Rates for Non-Convex Optimization”, Proceedings of 3rd International Conference on Learning Representations, pp. 340-345, 2015.

  • J. Duchi, E. Hazan, and Y. Singer, “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization”, Journal of Machine Learning Research, Vol. 12, pp. 2121-2159, 2011.

  • J. Kiefer and J. Wolfowitz, “Stochastic Estimation of the Maximum of a Regression Function”, The Annals of Mathematical Statistics, Vol. 23, No. 3, pp. 462-466, 1952.

  • Kaggle Dataset, Available at: https://www. kaggle.com/deadskull7/fer2013, Accessed at 2020.


Abstract Views: 360

PDF Views: 1




  • Facial Emotion Recognition Using Convolutional Neural Network

Abstract Views: 360  |  PDF Views: 1

Authors

Milan Tripathi
Department of Computer Engineering, Tribhuvan University, Nepal

Abstract


Facial expressions play a significant role in social communication since they convey a lot of information about people, such as moods, emotions, and other things. Many researchers gained an optimal accuracy in most of the popular facial recognition datasets: CK+, JAFFE, IEV, but in FER2013 the best model accuracy is about 74%. This article purpose deep learning-based models to mitigate this issue. Three models based on AlexNet, VGG19, and ResNet50 are used to train with the dataset, and the very best model among them is further analyzed. The best model is trained using various optimizers and evaluated based on its training and testing accuracy, confusion matrix, ROC Curve. The finest model gained an accuracy of 91.89504% which is better than past state of art models by at least 17% accuracy.

Keywords


Facial Expression, Confusion Matrix, Emotion, Optimizer, Receiver Operating Characteristic Curve.

References