Facial Expression Recognition Based on the FER2013 Dataset
DOI:
https://doi.org/10.53469/wjimt.2024.07(05).07Keywords:
Facial expression recognition, FER2013 dataset, Convolutional neural network, Covariance, Teaching supervisionAbstract
Facial emotions are a way to express one's thoughts and also an effective way to understand the emotions of others. Nowadays, with the rapid development of technology, computers can also recognize facial expressions through convolutional neural networks, deep learning, and other methods, and classify the results. Throughout the entire experiment, we chose FER2013 data as the training set for our model, which ultimately achieved an accuracy of around 62%. We also compared it with the SFEW dataset. The emergence of facial expression recognition will increase in the future, and its application in teaching supervision is what we are exploring here. Its main function can be used for invigilation, attendance, checking class status, and so on.
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