Facial expression recognition for human-computer interaction
This project proposes the development of a robust Facial Expression Recognition (FER) system integrated within a chatbot framework, aimed at assisting individuals in recognizing and interpreting their emotional states more effectively. The system employs a deep learning-based approach, specifical...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7239/1/fyp_CS_2025_TYS.pdf http://eprints.utar.edu.my/7239/ |
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| Summary: | This project proposes the development of a robust Facial Expression Recognition (FER) system
integrated within a chatbot framework, aimed at assisting individuals in recognizing and
interpreting their emotional states more effectively. The system employs a deep learning-based
approach, specifically utilizing the EfficientNet architecture, to improve the accuracy and
reliability of emotion detection. It integrates advanced Convolutional Neural Network (CNN)
techniques with preprocessing strategies to address variations in lighting conditions, patient
demographics, and facial occlusions, thereby ensuring consistent performance across diverse
clinical environments. Furthermore, the system delivers real-time feedback and personalized
guidance based on the detected emotions, fostering more empathetic and patient-centered care.
To achieve efficient real-time processing on resource-constrained medical devices, the model
incorporates optimization techniques such as pruning, quantization, and lightweight CNN
architectures. At the end, this project successfully trained a robust Facial Expression
Recognition (FER) model with approximately 73% accuracy, integrated it with a chatbot
system, and deployed the complete solution on a website to enable real-time, emotion-aware
interactions. |
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