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@article{168243,
        author = {Vaishnavi Thote and Chetna Satla and Samiya Pervez},
        title = {EMOJIFY: CREATE YOUR OWN EMOJI WITH DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {304-306},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168243},
        abstract = {In this paper, we use the FER2013 dataset to feed a convolutional neural network (CNN) architecture that can differentiate emotion from pictures. The CNN model is built using Kera’s layers, and the facial expressions are classified using a deep learning model. After that, the emotion will be assigned to an emoji or an avatar. We put our models to the test by creating a real-time vision machine that employs our proposed CNN architecture to do face recognition, emotion classification, and emoji mapping all in one combined phase. In this review paper, we give a complete study of the current body of work on emoji, looking at how they've evolved, how they're used differently, what purposes they have, and what research has been done on them.},
        keywords = {Convolution Neural Network (CNN), face detection, emotion classification, emoji mapping, CNN architecture.},
        month = {October},
        }