A Comparative Analysis Between Machine and Deep Learning Models for Facial Emotion Recognition

DOI

10.2991/978-94-6463-512-6_40How to use a DOI?

Keywords

Emotion Recognition; Support Vector Machine; Convolutional Neural Networks

Abstract

Facial emotion recognition is vital for enhancing human-computer interactions, providing personalized services, and diagnosing mental health conditions. This technology holds promise for improving accessibility and emotional intelligence in various fields, from healthcare to education. In recent years, Artificial Intelligence (AI) has made significant improvements in emotion recognition, particularly in the development of facial emotion classification models. Deep learning dominates this domain, yet conventional machine learning approaches, e.g. Support Vector Machine (SVM), still hold value in applications. Each model has its strengths and weaknesses, necessitating a choice based on specific needs. At the forefront, neural network models like Convolutional Neural Networks (CNNs) have excelled in facial expression recognition, with numerous studies enhancing recognition accuracy through optimized algorithms. However, AI-based emotion recognition still faces challenges of data volume and interpretability. Looking ahead, AI emotion recognition holds broad application prospects in areas like mental health and education, promising to bring more convenience to human society.

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  - Genghao Du
PY  - 2024
DA  - 2024/09/23
TI  - A Comparative Analysis Between Machine and Deep Learning Models for Facial Emotion Recognition
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 373
EP  - 379
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_40
DO  - 10.2991/978-94-6463-512-6_40
ID  - Du2024
ER  -