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@article{179070,
        author = {S. Gunaseelan and R. Praveena and V. Bavan Kumar and S. Diwahar and S. Suriya Moorthi},
        title = {Emotion Detection with Facial Feature Recognition Using CNN & OPENCV},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7070-7072},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179070},
        abstract = {Facial expressions play a critical role in human interaction, acting as a universal medium of emotional exchange. This study introduces an advanced Facial Emotion Recognition (FER) framework powered by deep learning methodologies. It accurately distinguishes seven core emotional states—joy, sorrow, anger, fear, disgust, astonishment, and neutrality—by processing facial images. Leveraging a combination of preprocessing, feature extraction, and classification through Convolutional Neural Networks (CNNs), the system is developed in Python using OpenCV, Keras, and TensorFlow. Trained on widely recognized datasets, it delivers strong performance even under diverse conditions, indicating high practical viability.},
        keywords = {Facial Emotion Recognition, CNN, OpenCV, Deep Learning, Python, Image Classification},
        month = {May},
        }