Facial Emotion Recognition Using Transfer Learning

DOI

10.2991/978-94-6463-858-5_64How to use a DOI?

Keywords

Facial Emotion Recognition; Transfer Learning; Deep Learning; EfficientNet; Self-Attention; SVM

Abstract

Facial Emotion Recognition (FER) plays a crucial role in applications such as human-computer interaction, affective computing, and mental health monitoring. This paper proposes a novel methodology that combines EfficientNet-based feature extraction, Self-Attention-based feature refinement, and Support Vector Machine (SVM) classification to achieve robust and high-performance emotion recognition. Experimental results demonstrate the superiority of this hybrid approach over traditional classifiers, showing improved accuracy, precision, and recall on standard FER datasets. Furthermore, this paper discusses various challenges, including dataset limitations, environmental conditions, and computational efficiency. Mathematical formulations, diagrams, and performance evaluations are included for in-depth analysis.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shaik Mohammad Saabir
AU  - Muppuri Namitha
AU  - Samvrant Samal
AU  - Bidyutlata Sahoo
PY  - 2025
DA  - 2025/11/04
TI  - Facial Emotion Recognition Using Transfer Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 753
EP  - 763
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_64
DO  - 10.2991/978-94-6463-858-5_64
ID  - Saabir2025
ER  -