Facial Emotion Recognition Project Outline:
• Objective:
o Build a model to classify facial expressions in images into different emotions (e.g., happy, sad, angry, surprised).
Explore techniques for handling variations in lighting, pose, and image quality.

1. Setup:
o Python environment with TensorFlow/PyTorch, etc.
o Download FER2013 or similar dataset.

2. Data Prep:
o Load and explore data.
o Preprocess: Resize, normalize, augment, split.

3. Model:
o Traditional ML models.
o CNN architecture, etc.

4. Evaluation and Refinement:
o Evaluate on test set, generate confusion matrix.
o Fine-tune hyperparameters, optimize models.

5. Handling Variations:
o Augment for lighting, pose, quality.
o Consider attention mechanisms, robust feature extraction.

6. Conclusion and Future Work:
o Summarize findings, best model, techniques.