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Advancements, Challenges, and Recent Trends in Facial Expression Recognition Systems: A Comprehensive Review

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
C. Sheeba Joice
S. Hitha Shanthini
C. Jenisha
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the advancements, challenges, and recent trends in facial expression recognition systems, focusing on deep learning methods. It covers a wide range of topics, including the importance of facial expression recognition in various fields such as security, healthcare, education, and corporate setups. The text explores different types of deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and their applications in facial expression recognition. It also discusses the significance of preprocessing techniques, feature extraction methods, and the role of classifiers in improving the accuracy of these systems. The chapter highlights the use of various datasets like CK+, JAFFE, and AffectNet for training and evaluating facial expression recognition models. Additionally, it addresses the challenges and ethical considerations associated with this technology. The conclusion emphasizes the promising future of human-computer interaction driven by advancements in facial expression recognition technology.
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previous chapter Hybrid Deep Learning for Meme Sentiment and Emotion Analysis Using LLMs
next chapter Hyperspectral Image Feature Extraction Using A Light Bidirectional Encoder Representations from Transformers
Title
Advancements, Challenges, and Recent Trends in Facial Expression Recognition Systems: A Comprehensive Review
Authors
C. Sheeba Joice
S. Hitha Shanthini
C. Jenisha
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_30
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