Enhancing Emotion Detection Through CNN-Based Facial Expression Recognition

DOI

10.2991/978-94-6463-540-9_100How to use a DOI?

Keywords

Emotion Detection; Convolutional Neural Networks (CNN); Identify Weaknesses; Preprocessing Techniques

Abstract

Artificial intelligence-based approaches, such as Convolutional Neural Networks (CNN), hold significant promise for emotion detection, particularly in facial expression recognition, offering invaluable insights for various sectors including business, medicine, and psychology. This paper explores the utilization of CNN for facial feature extraction to discern emotions, employing preprocessing procedures to standardize images sourced from the Kaggle website. Methods including blurring, scaling, contour image alteration, and normalization are employed for standardization, facilitating accurate feature extraction and emotion detection. Despite the separation of classification and feature extraction phases in CNN, which necessitated extensive effort to enhance performance, the technique ultimately offers superior accuracy compared to traditional classifiers. However, challenges arise from noisy and deviated images in the dataset, impacting the efficacy of the CNN model. To mitigate these challenges, preprocessing techniques such as grayscale conversion, resizing, and normalization are applied to standardize dataset images. The research aims to identify weaknesses in existing models and develop improvements to conventional emotion detection techniques. Experimental results underscore the value of combining these techniques for precise prediction of facial expressions, contributing to advancements in emotion detection methodologies.

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  - Jinyang Wang
PY  - 2024
DA  - 2024/10/16
TI  - Enhancing Emotion Detection Through CNN-Based Facial Expression Recognition
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 1003
EP  - 1010
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_100
DO  - 10.2991/978-94-6463-540-9_100
ID  - Wang2024
ER  -