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@article{174362,
        author = {Ammireddy Vasantha Kumar},
        title = {Facial Expression Recognition using Ensemble  Learning: ResNet50 and MobileNetV2},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3563-3568},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174362},
        abstract = {Facial Expression Recognition using Ensemble Learning is a cutting-edge application that utilizes machine learning and deep learning to recognize human emotions through facial expressions. This project leverages two robust pre-trained models, MobileNetV2 and ResNet50, which have been fine-tuned on the FER+ dataset to provide precise and efficient emotion classification. The system is built for real-time emotion recognition, identifying feelings such as anger, disgust, fear, happiness, sadness, surprise, and neutrality using a live webcam feed. The implementation combines computer vision methods for face detection using OpenCV's Haar cascades with deep learning models for classifying emotions. MobileNetV2, recognized for its lightweight design and efficiency, processes 224x224 RGB facial images to enable quick inference.},
        keywords = {Artificially intelligence (AI), Facialemotion recognition (F E R) , Convolutional neural networks (CNN), Rectified linear units (ReLu), Deep learning (DL).},
        month = {March},
        }