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Emotion recognition from occluded facial images using deep ensemble model.

Ullah, Zia; Mohmand, Muhammad Ismail; ur Rehman, Sadaqat; Zubair, Muhammad; Driss, Maha; Boulila, Wadii; Sheikh, Rayan; Alwawi, Ibrahim

Authors

Zia Ullah

Muhammad Ismail Mohmand

Sadaqat ur Rehman

Muhammad Zubair

Maha Driss

Wadii Boulila

Rayan Sheikh

Ibrahim Alwawi



Abstract

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.

Citation

ULLAH, Z, MOHAMAND, M.I., UR REHUMAN, S., ZUBAIR, M., DRISS, M., BOULILA, W., SHEIKH, R. and ALWAWI, I. 2022. Emotion recognition from occluded facial images using deep ensemble model. Computers, materials and continua [online], 73(3), pages 4465-4487. Available from: https://doi.org/10.32604/cmc.2022.029101

Journal Article Type Article
Acceptance Date May 24, 2022
Online Publication Date Jul 25, 2022
Publication Date Jul 25, 2022
Deposit Date Aug 11, 2022
Publicly Available Date Aug 11, 2022
Journal Computers, materials and continua
Print ISSN 1546-2218
Electronic ISSN 1546-2226
Publisher Tech Science Press
Peer Reviewed Peer Reviewed
Volume 73
Issue 3
Pages 4465-4487
DOI https://doi.org/10.32604/cmc.2022.029101
Keywords Ensemble learning; Emotion recognition; Feature fusion; Occlusion
Public URL https://rgu-repository.worktribe.com/output/1734672

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