Input Image → Face Detection → Preprocessing → Emotion Classification Model → Confidence Scores → Output
- Image normalization and resizing
- Data augmentation (rotation, flipping, brightness variation)
- Convolutional Neural Network (CNN) / transfer learning architecture
- Optimization using cross-entropy loss
- An image is provided to the system
- Facial region is detected and extracted
- Preprocessed image is passed through the trained model
- Emotion probabilities are generated
- Highest-confidence emotion is returned to the user
- Batch inference for offline analysis
- GPU acceleration for training and inference
- Potential edge deployment using model quantization
- Accuracy versus inference latency
- Model complexity versus interpretability
- Cloud-based inference versus edge-based deployment