Facial Emotion Recognition And Detection Using Convolutional Neural Network

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dc.contributor.authorJanet O. Jooda
dc.date.accessioned2025-05-27T11:06:17Z
dc.date.available2025-05-27T11:06:17Z
dc.date.issued2024
dc.description.abstractHuman 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.
dc.identifier.citationAdebimpe 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
dc.identifier.urihttps://repository.run.edu.ng/handle/123456789/4808
dc.language.isoen
dc.publisherJournal of Computing, Science &Technology
dc.relation.ispartofseriesVol. 1; Issue 2
dc.titleFacial Emotion Recognition And Detection Using Convolutional Neural Network
dc.typeArticle
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