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Facial emotion recognition
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- Author(s) / Creator(s)
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Facial emotion recognition has gained substantial attention in past decade’s as a key factor for human-computer interaction. Emotion recognition refers to the ability to identify and understand human emotions based on various cues, such as facial expressions, voice tone, body language, and physiological signals. This report presents a comprehensive study of facial emotion recognition using the FER2013+ dataset, providing an overview of its structure, size and different emotion categories. There are several deep learning architectures for facial emotion recognition, the method that is used for this project is Convolutional Neural Network (CNN). The model is implemented and trained on the FER2013+ dataset, and their performance are evaluated using various metrics like precision, recall, f1-score. The report also explores real-worlds applications of facial emotion recognition including emotion-aware user interfaces, investigation purposes, personalized recommendation systems, and mental health monitoring tools.
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- Date created
- 2023
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- Subjects / Keywords
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- Type of Item
- Research Material