Deep Learning and Machine Learning Based Facial Expression Recognition Employed in Mental Health

Hanyu Lin

2024

Abstract

Facial expressions play an important role in human communication, conveying a wide range of emotions without the need for verbal communication. In recent years, Facial Expressions Recognition (FER) has found applications in various domains, particularly in the medical field. The technology was originally developed, using mostly machine-learning model algorithms, including Support Vector Machine (SVM) etc. However, as the dimension of the characteristics increases, it is difficult to obtain more feature samples. To solve problem, a framework for Principal Components Analysis (PCA)+Latent Dirichlet Allocation (LDA) is proposed. After a few years, the development of deep learning gave the FER experiments many large models to use, such as Convolutional Neural Network (CNN). However, the complexity of facial expressions, compounded by factors such as illumination, posture, and occlusion, poses challenges for accurate recognition using traditional methods, the Long-Short-Term-Memory (LSTM) layer structure is added to the CNN, and it developed into a new model called LSTM-CNN. Deep learning excels in handling complex data and large- scale datasets, making it the preferred choice for FER due to its adaptability and end-to-end learning capability. However, its lack of interpretability and challenges with complex data can hinder accuracy and trust in results, especially in medical applications like mental health diagnosis. Preprocessing data and refining identification algorithms are crucial steps to improve accuracy in FER projects.

Paper Citation

in Harvard Style

Lin H. (2024). Deep Learning and Machine Learning Based Facial Expression Recognition Employed in Mental Health. 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 285-288. DOI: 10.5220/0012936800004508

in Bibtex Style

@conference{emiti24,
author={Hanyu Lin},
title={Deep Learning and Machine Learning Based Facial Expression Recognition Employed in Mental Health},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={285-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012936800004508},
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 - Deep Learning and Machine Learning Based Facial Expression Recognition Employed in Mental Health
SN - 978-989-758-713-9
AU - Lin H.
PY - 2024
SP - 285
EP - 288
DO - 10.5220/0012936800004508
PB - SciTePress