Facial Emotion Recognition And Detection Using Convolutional Neural Network
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Date
2024
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Journal of Computing, Science &Technology
Abstract
Human emotions have important role in communication especially to understand the emotions of those with
speech problems. Various facial emotion recognition and detection systems have been developed but most of
these systems have difficulty in performing a muti-class classification and yielded lower accuracy. Therefore,
this research employed convolutional neural network for recognition and detection of four basic emotions: happy, sad, angry,
neutral. The dataset training the convolutional neural network model was obtained human emotion, accuracy, machine locally and it include about 133 images.
Results show that the system developed performed well with an learning, communication, detection
accuracy of 0.9533, precision of 0.97, F1-score of 0.94 and recall of 0.93. The approach used showed a
significant improvement over traditional machine learning methods and be a useful tool for those with speech
problems and visually to predict human emotion.
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Citation
Adebimpe Esan, Adedayo Sobowale, Janet Jooda, Tomilayo Adebiyi, Michael Adio, & Daniel Oladapo (2024). Facial Emotion Recognition and Detection Using Convolutional Neural Network, Journal of Computing, Science &Technology, Vol.1, Issue 2, Pp 1 - 5