Advancements and Challenges In Emotion Recognition Technologies
DOI:
https://doi.org/10.56294/gr202588Keywords:
Emotion recognition, Human-computer interaction, Machine learning, Deep learning, AlgorithmsAbstract
Introduction; Emotion recognition is a transformative technology that enhances human-computer interaction by enabling systems to interpret and respond to human emotions effectively.
Objective; This paper investigates the current landscape of emotion recognition technologies, emphasizing the diverse sources of emotional data, including facial expressions, voice, physiological signals, and textual content.
Method; We explore the methodologies and algorithms employed for emotion classification, ranging from traditional machine learning techniques to advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Result; This study provides a comparative analysis of various emotion recognition approaches, evaluating their accuracy, robustness, and computational efficiency.
Conclusion; This paper contributes to the ongoing discourse on emotion recognition by offering a comprehensive overview of current trends, challenges, and opportunities for advancing the field.
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Copyright (c) 2025 Anamika Kumari, Smita Pallavi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
