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Emotion recognition in video using deep learning

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dc.contributor.author Hiba, Zohra
dc.contributor.author Taleb Ahmed, Soumia
dc.date.accessioned 2022-05-29T11:06:49Z
dc.date.available 2022-05-29T11:06:49Z
dc.date.issued 2021
dc.identifier.uri https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1054
dc.description.abstract Affective computing aims to implement methods and technologies to recognize and synthesize human emotions. Understanding human facial expressions is essential to the success of this new branch of AI. Emotions can be conveyed through various channels, the most prominent are facial expressions, speech, texts and various other physiological signals. This topic has occupied researchers for a long time due to the difficulty of understanding and categorizing these expressions. In this work, we explore the different techniques carried out to recognize facial emotions in videos. We experiment on the AFEW dataset with two models based on deep learning. The first uses TCNs and the second uses CNNs. The experience with the first model was very hard since it belongs to recent sequential models and was not completed due to difficulty of implementation and limited resources. The second model achieved good accuracy of up to 91%. EN_en
dc.publisher université Ghardaia EN_en
dc.subject Facial Emotion recognition, Deep Learning, TCN, CNN, AFEW database. EN_en
dc.title Emotion recognition in video using deep learning EN_en
dc.type Thesis EN_en


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