Live Facial Emotion Detection (trained CNN on labeled face images)
Developed and trained an emotion classification model using a dataset of labeled facial images. Performed data augmentation and model training to improve generalization for real-time facial emotion inference. Evaluated model performance using classification accuracy metrics and integrated the trained model into a live detection pipeline. • Used labeled images of human faces for supervised learning. • Applied data augmentation during training. • Built a live emotion detection system that performs real-time inference. • Achieved 92% emotion classification accuracy.