International Journal on Science and Technology

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A Systematic Review on Facial Emotion Recognition System Datasets

Author(s) Ramanpreet Kaur, Dr. Kanwal Preet Singh Attwal, Dr. Harmandeep Singh
Country India
Abstract This review provides a comprehensive analysis of prominent facial emotion recognition (FER) datasets, including RAVDESS, FER2013, AffectNet, CK+, JAFFE, SFEW, EmotioNet, and RAF-DB. Each dataset is examined based on its size, emotional categories, demographic coverage, and relevance to real-world scenarios. While these datasets have significantly contributed to the development and benchmarking of FER models, notable limitations persist, particularly the underrepresentation of diverse demographic groups and the lack of complex or subtle emotional expressions. The review further explores emerging deep learning advancements such as Transformer-based architectures, multimodal learning techniques, and generative models, which hold promise in overcoming these challenges. By integrating these innovations with enriched and inclusive datasets, FER systems can achieve greater accuracy, adaptability, and robustness in uncontrolled environments. Such improvements are essential for enhancing the performance and fairness of emotion recognition applications across domains including healthcare, education, customer service, and human-computer interaction.
Keywords Facial Emotion Recognition (FER), Deep Learning, Datasets, Convolutional Neural Networks (CNN), Machine Learning
Field Computer Applications
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-22
DOI https://doi.org/10.71097/IJSAT.v16.i2.6406
Short DOI https://doi.org/g9q4cm
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