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Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

Zia Ullah1, Muhammad Ismail Mohmand1, Sadaqat ur Rehman2,*, Muhammad Zubair3, Maha Driss4, Wadii Boulila5, Rayan Sheikh2, Ibrahim Alwawi6

1 Department of Computer Science, The Brains Institute, Peshawar, 25000, Pakistan
2 School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK
3 Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium
4 Security Engineering Laboratory, CCIS, Prince Sultan University, Riyadh, 12435, Saudi Arabia
5 Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435, Saudi Arabia
6 Department of Computer Science, Robert Gordon University, Aberdeen, UK

* Corresponding Author: Sadaqat ur Rehman. Email:

Computers, Materials & Continua 2022, 73(3), 4465-4487.


Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.


Cite This Article

Z. Ullah, M. I. Mohmand, S. Ur Rehman, M. Zubair, M. Driss et al., "Emotion recognition from occluded facial images using deep ensemble model," Computers, Materials & Continua, vol. 73, no.3, pp. 4465–4487, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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