Comparative Analysis of Facial Emotion Recognition

Prerak Khandelwal*, Aaryan Pimple**, Devang Punatar***, Ashwini Patil****
*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, India.
Periodicity:April - June'2023


This paper provides an overview of the phases, methods, and datasets used in modern Facial Emotion Recognition (FER). FER has been a crucial topic in computer vision and Machine Learning (ML) for decades. By using Convolutional Neural Networks (CNN) to recognize facial expressions, valuable insights into people's emotional states can be gained, leading to improved services such as personalized healthcare, enhanced customer service, and more effective marketing. Automated FER can be used in various settings, including healthcare, education, criminal investigations, and Human Robot Interface (HRI). The study includes a comparative analysis of the performance and conclusions of several models such as Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50), MobileNet, Deep CNN and the proposed pretrained VGG 16 architecture. These models can be integrated into different systems for various purposes such as obtaining feedback on products, services, or virtual learning platforms. Ultimately, Facial Emotion Recognition using Convolutional Neural Networks (CNN) can help reduce bias in decision-making processes by providing an unbiased assessment of a person's emotional state.


CNN, Facial Expression Recognition, Facial Feature Extraction, Expression Classification.

How to Cite this Article?

Khandelwal, P., Pimple, A., Punatar, D., and Patil, A. (2023). Comparative Analysis of Facial Emotion Recognition. i-manager’s Journal on Image Processing, 10(2), 37-49.


[3]. EmptyEkman, P. (2013). Emotion in the Human Face. Malor Books, USA.
[5]. EmptyHe, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).
[9]. EmptyKumar, B. A., & Kumar, M. A. (2017). Efficient DFTbased channel estimation for OFDM Systems on multipath channels. International Journal of Conceptions on Computing and Information Technology, 5(1), 32-35.
[14]. EmptyPunatar, D., Pimple, A., Khandelwal, P. (2022). Emotion recognition using facial expressions. International Journal for Scientific Research & Development, 10(9), 79-82.
[17]. EmptySiddiqi, M. H., Alruwaili, M., Bang, J., & Lee, S. (2017). Real time human facial expression recognition system using smartphone. International Journal of Computer Science and Network Security, 17(10), 223-230.
[19]. EmptySitaula, C., & Ghimire, N. (2017). An analysis of early stopping and dropout regularization in deep learning. International Journal of Conceptions on Computing and Information Technology, 5(1), 17-20.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.