Publication Date
Fall 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Genya Ishigaki
Third Advisor
Thomas Austin
Keywords
ML, CNN, MobileNetV2, VGG16, SVM
Abstract
Facial expression classification is a powerful tool for understanding human emotions, with applications spanning human-computer interaction, healthcare, and entertainment. By analyzing facial cues, systems can interpret emotional states and adapt their responses, creating more personalized and emotionally aware experiences. One emerging application of facial expression classification is in music recommendation systems, where user emotions are integrated to suggest music that aligns with their current mood. While prior research has primarily classified facial expressions into four emotion categories, this study broadens the scope to seven emotions: angry, disgust, fear, happy, neutral, sad, and surprise. The project evaluates four machine learning techniques—CNN, MobileNetV2, VGG16, and SVM—combined with various data preprocessing methods to identify the most effective model for facial expression classification. Results indicate that CNN achieves the highest accuracy at 65.6%, followed by MobileNetV2 and VGG16, used for transfer learning, with accuracies of 61.62% and 57.55%, respectively. In comparison, SVM achieves a 45.5% accuracy, highlighting the superior performance of deep learning models for this task and paving the way for more personalized and emotionally intelligent music recommendation systems.
Recommended Citation
Li, Tiantong, "Facial Expression Mood Classification Using Machine Learning" (2024). Master's Projects. 1445.
https://scholarworks.sjsu.edu/etd_projects/1445
