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- W4315565736 abstract "Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering. The use of convolutional networks includes a learning function. This looks very promising for this task where the functionality is not easy to define. Additionally, the network he was evaluated using two different corpora. One was used during network training and also helped tune parameters and define the network architecture. This corpus consisted of mimetic emotions. The network that yielded the highest classification accuracy results was tested on the second dataset. Although the network was trained on only one corpus, the network reported promising results when tested on another dataset showing non-real facial emotions. The results achieved did not correspond to the state of the art. Collected evidence indicates that deep learning may be suitable for facial expression classification. Deep learning therefore has the potential to improve human-machine interaction. Because the ability to learn functions allows machines to evolve cognition. And through perception, the machine could offer a smoother response, greatly improving the user's experience." @default.
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- W4315565736 date "2023-01-31" @default.
- W4315565736 modified "2023-09-27" @default.
- W4315565736 title "Facial Emotion Detection Using Deep Learning" @default.
- W4315565736 doi "https://doi.org/10.22214/ijraset.2023.48585" @default.
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