Survey on Deep Learning Based Face Expression Recognition Methods

DOI

10.2991/978-94-6463-512-6_48How to use a DOI?

Keywords

Face Recognition; Deep Learning; Convolutional Neural Network; Feature Extraction

Abstract

In recent years, the recognition of facial expressions has become a highly popular area of study, carrying significant importance for the advancement of scientific and technological progress. Face expression recognition has a very wide and practical application in many fields such as medical treatment, transportation, education and so on. This paper initially defines face recognition technology and examines its advancements. Following that, the operation and recognition efficiency of CNN and FaceNet, two deep learning-based face recognition techniques, are examined and analyzed. Subsequently, the two extraction techniques—conventional feature extraction and deep learning-based feature extraction—are presented together with their respective benefits and drawbacks. Finally, combined with the current development trend, the future development direction of face recognition technology based on image fusion is proposed, which is expected to combine the information from different image sources to improve the accuracy and robustness of recognition, and summarize and prospect the development of face recognition technology.

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  - Xiang Qi
PY  - 2024
DA  - 2024/09/23
TI  - Survey on Deep Learning Based Face Expression Recognition Methods
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 453
EP  - 458
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_48
DO  - 10.2991/978-94-6463-512-6_48
ID  - Qi2024
ER  -