Emotionscan: Facial recognization and mood detection system
Degree
Master of Science in Computer Science
Department
Department of Computer Science
School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Fall 2024
Supervisor
Dr. Muhammad Sarim, Visiting Faculty, Department of Computer Science, School of Mathematics and Computer Science (SMCS)
Keywords
Emotion recognition, CNN, DDAMFN++, AffectNet, deep learning, Spotify integration, Emotional well-being
Abstract
Human emotions, as reflected through facial expressions, provide a vital medium for understanding psychological states and overall well-being. This research focuses on developing a real-time mood detection system utilizing advanced deep learning techniques, including Convolutional Neural Networks (CNNs) and the Dynamic Dual Attention Mixed Feature Network++ (DDAMFN++) model. By leveraging the AffectNet dataset, a comprehensive collection of annotated facial expressions, the system identifies emotions such as happiness, sadness, anger, and neutrality with remarkable accuracy. The DDAMFN++ model incorporates dynamic attention mechanisms to effectively analyze spatial and temporal features, while CNNs enable precise feature extraction from facial expressions. Integrated with OpenCV for real-time video processing and Spotify's API for mood-based music recommendations, the system bridges cutting-edge technology with practical applications. A robust token management framework supports seamless integration with external services. This project represents a significant step toward creating responsive digital environments that enhance emotional well-being. By delivering accurate emotion detection and actionable interventions, it provides a novel solution for real-time mental health support and personalized user experience
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Sultan, Abdul Sami. "Emotionscan: Facial recognization and mood detection system." Unpublished graduate research project. Institute of Business Administration. 2024. https://ir.iba.edu.pk/research-projects-mscs/63
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