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

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 28, 2022
• Publication Date: Dec 31, 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