Emotion Classification (CV Project)

Developed a facial emotion recognition application using MobileNet and ResNet with transfer learning to classify seven emotional states. Preprocessed and augmented a large labeled dataset (30,841 training images and 7,066 validation images) and integrated MediaPipe for accurate face detection. Achieved up to 70% classification accuracy and evaluated model performance using a confusion matrix.