Facial Emotion Detection using CNN & VGG16 Model

In fields like human-computer interaction, healthcare, and marketing, facial and emotional recognition utilizing machine learning (ML) has found many uses. The primary method used in this study is Convolutional Neural Networks (CNNs), more especially pre-trained models like ResNet and VGG16 that have been tweaked using labelled facial expression datasets. Enhancement techniques are used to enhance model generalization, and data preparation, ensemble learning, and transfer learning are used to address issues like posture variations and cultural expressions. In tests conducted on the benchmark datasets CK+ and FER-2013, competition-level performance is displayed in parameters such as accuracy, precision, recall, and F1-score. The application of this ML-based methodology in emotion-aware systems, human-computer interaction, and mental health monitoring is highlighted by these results.The present work underscores the potential of machine learning (ML) in detecting facial expressions and emotions. Further research is necessary to improve the functionality of these systems in order to achieve greater social advantages.