Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions

Pamod D, Charles J, Hewarathna AI, Vigneshwaran P, Lekamge S, Thuseethan S (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Communications in Computer and Information Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 2026 CCIS

Pages Range: 387-402

Conference Proceedings Title: Communications in Computer and Information Science

Event location: Derby GB

ISBN: 9783031530814

DOI: 10.1007/978-3-031-53082-1_31

Abstract

The ability to recognise and interpret emotional expressions is crucial since emotions play a significant role in our daily lives. Emotions are multifaceted phenomena that affect our behavior, perception, and cognition. As a result, numerous machine-learning and deep-learning algorithms for emotion analysis have been studied in previous works. Finding emotion in an obscured face, such as one covered by a scarf or hidden in shadow, is considerably harder than in a complete face, though. This study explores the effectiveness of deep learning models in occluded facial emotion analysis through a transfer learning approach. The performance of two individual pre-trained models, MobileNetV2 and EfficientNetB3, is compared alongside a hybrid model that combines both approaches. This comparison is conducted using the FER-2013 dataset. The dataset consists of 35,887 images and categorizes emotions into seven emotional categories. The results indicate that the hybrid model attained the highest accuracy, with a score of 93.04% for faces occluded at the top and 92.63% for faces occluded at the bottom. Additionally, the study suggests that top-occluded faces displayed more pronounced emotional expressions in comparison to bottom-occluded faces. Overall, these findings imply that hybrid architecture, which was developed as a state-of-the-art model in the study, proves to be effective for analyzing emotions in facial expressions that are partially obscured.

Involved external institutions

Sabaragamuwa University of Sri Lanka / சபரகமுவா பல்கலைக்கழகம் / ශ්‍රී ලංකා සබරගමුව විශ්වවිද්‍යාලය LK Sri Lanka (LK) Deakin University AU Australia (AU)

How to cite

APA:

Pamod, D., Charles, J., Hewarathna, A.I., Vigneshwaran, P., Lekamge, S., & Thuseethan, S. (2024). Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions. In KC Santosh, Aaisha Makkar, Myra Conway, Ashutosh K. Singh, Antoine Vacavant, Anas Abou el Kalam, Mohamed-Rafik Bouguelia, Ravindra Hegadi (Eds.), Communications in Computer and Information Science (pp. 387-402). Derby, GB: Springer Science and Business Media Deutschland GmbH.

MLA:

Pamod, Dilshan, et al. "Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions." Proceedings of the 6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023, Derby Ed. KC Santosh, Aaisha Makkar, Myra Conway, Ashutosh K. Singh, Antoine Vacavant, Anas Abou el Kalam, Mohamed-Rafik Bouguelia, Ravindra Hegadi, Springer Science and Business Media Deutschland GmbH, 2024. 387-402.

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