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@article{205377,
        author = {Manisha Bhaskar More and Dr.Bali Thorat and Samadhan Ghodke},
        title = {Deep Learning Approaches to Facial Emotion Recognition in Smart Classrooms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6811-6818},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205377},
        abstract = {The integration of facial emotion detection with automated attendance systems and adaptive feedback mechanisms has gained increasing attention in intelligent educational environments. This review paper examines recent advancements in facial expression recognition (FER), biometric attendance systems, and emotion-aware learning models applied in classroom settings. The study analyses various methodologies, machine learning and deep learning architectures, datasets, and evaluation metrics used for detecting student emotions and managing attendance automatically. Research articles published between 2018 and 2025 were collected from major academic databases, including SciSpace, Google Scholar, ArXiv, and PubMed. Initially, more than 200 publications were identified, and after removing duplicates and irrelevant studies, 150 papers were selected for detailed analysis. The findings summarise current techniques, highlight practical implementations in education, and identify research gaps that may guide the development of more effective emotion-aware educational systems.},
        keywords = {Facial emotion detection, facial expression recognition, automated attendance, student engagement, adaptive learning, affective computing, deep learning, educational technology, CNN, real-time emotion recognition},
        month = {June},
        }