Advancements in Facial Expression Recognition: A Comparative Study of Traditional Machine Learning and Deep Learning Approaches

Yujie Jin

2024

Abstract

In recent years, the use of facial expression recognition technology has become widespread with the development of artificial intelligence. However, limitations in face expression recognition still exist due to various factors such as environment and angle. This paper explores the processing methods of machine learning and deep learning models for face recognition and compares the differences between the two methods. In traditional machine learning models, this paper analyses the methodology of combining the results of Coded Hidden Markov Model (CHMM) and Fundamental Hidden Markov Model (FHMM) firstly, and the aim is to devise a comprehensive framework of criteria for classifying samples. The paper then analyses the key role of k-value in KNN classifiers by varying the k-value. This reveals that the choice of the k-value significantly affects the accuracy of emotion classification. The text appears to already meet the desired characteristics. No changes have been made. In deep learning, various CNN configurations such as region-based CNN (R-CNN), faster R-CNN, and 3D CNN have been analyzed for their precision on different datasets. Additionally, the study explores the extraction of face information using FPN target detection methods and the integration of LSTM networks with CNNs to efficiently capture sequential information from facial images and extract a more comprehensive representation of features. It is concluded that deep learning model is more effective and face emotion recognition is transitioning from traditional machine learning to deep learning.

Paper Citation

in Harvard Style

Jin Y. (2024). Advancements in Facial Expression Recognition: A Comparative Study of Traditional Machine Learning and Deep Learning Approaches. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 311-315. DOI: 10.5220/0012937300004508

in Bibtex Style

@conference{emiti24,
author={Yujie Jin},
title={Advancements in Facial Expression Recognition: A Comparative Study of Traditional Machine Learning and Deep Learning Approaches},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={311-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012937300004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Advancements in Facial Expression Recognition: A Comparative Study of Traditional Machine Learning and Deep Learning Approaches
SN - 978-989-758-713-9
AU - Jin Y.
PY - 2024
SP - 311
EP - 315
DO - 10.5220/0012937300004508
PB - SciTePress