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@article{154371,
        author = {G. Sumalatha},
        title = {FACIAL EXPRESSION RECOGNITION USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {6-11},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154371},
        abstract = {Automatic expression recognition based on facial expression is a fascinating study area that has been presented and utilized in a variety of fields, including safety, health, and human-machine interactions. Researchers in this subject are interested in developing strategies to understand, code, and extract facial expressions in order to improve computer prediction. Facial emotion recognition is a subset of facial recognition that is becoming in popular as the need for it grows. Though there are methods for identifying expressions using data science techniques like machine learning and deep learning, this work seeks to recognize expressions and classify them based on photographs utilizing deep learning and image classification methods. In this case we are going to use CK+48 dataset and going to build deep learning models. First we do image processing after doing image processing split the data in 80:20 or 70:30 ratio. By using train we train the model then test it on test data. In this paper we build CNN and VGG-16 model. The goal of this project based on images we need to predict face emotion. In this by using deep learning model we can predict with 98% accuracy.},
        keywords = {Deep learning, Image processing, CNN, VGG-16, Transfer learning.},
        month = {},
        }