Multiple Optimized Deep Learning Models for Effective Facial Expression

DOI

10.2991/978-94-6463-540-9_62How to use a DOI?

Keywords

Facial expression recognition; deep learning; artificial intelligence

Abstract

Facial expression recognition is an essential domain within computer vision, focused on interpreting human emotions through facial cues for enhanced human-computer interaction. This study examines the current state and challenges in facial expression recognition, emphasizing the role of deep learning architectures like CNNs, ResNet, and VGG in driving advancements in this field. These models have improved system performance by enabling more precise feature extraction and efficient pattern recognition. However, the generalization of these systems to diverse, real-world environments remains a significant challenge due to factors like inconsistent lighting, occlusions, and varied facial orientations. This research contributes to overcoming these limitations by proposing a novel deep learning-based architecture that optimizes the recognition process across different scenarios and demographic variances. The study leverages extensive datasets like FER2013 and incorporates advanced model training techniques, including transfer learning, to enhance the robustness and accuracy of facial expression recognition systems. By addressing these challenges, the study aims to refine the technology to be more adaptive and sensitive to a wide array of emotional expressions, thereby supporting the development of more intuitive and engaging user interfaces that can integrate seamlessly into daily human interactions and applications. This will potentially revolutionize interactions within digital environments, making them more humane and responsive to emotional feedback.

Copyright

© 2024 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  - Ruoyu Li
PY  - 2024
DA  - 2024/10/16
TI  - Multiple Optimized Deep Learning Models for Effective Facial Expression
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 621
EP  - 627
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_62
DO  - 10.2991/978-94-6463-540-9_62
ID  - Li2024
ER  -