Facial Expression Recognition Comparative Study

I need a full, end-to-end study that benchmarks both classical machine-learning techniques and modern deep-learning architectures on the FER2013 dataset. The work starts with thoughtful preprocessing, continues through careful hyper-parameter tuning, and finishes with a thorough academic-style analysis.

Scope of Work
• Run an ablation study so we understand the impact of each preprocessing step and model component.
• Train and validate multiple models, then evaluate them with Accuracy, F1-score, Recall, and detailed confusion matrices.
• Carry out a data-efficiency test (using just 10 % of the training set versus the full 100 %).
• Test robustness by adding Gaussian noise, blur, and gamma shifts at three severity levels.
• Compare models on performance metrics, training time, and inference speed, wrapping everything in clear visualisations and commentary.

Deliverables
1. Well-commented Python code in a Jupyter Notebook.
2. Trained weight files for every model.
3. All raw results, plots, and figures.
4. A comprehensive academic report in English explaining methodology, findings, and future work.
5. A concise PowerPoint deck that highlights key insights.

I am looking for reproducible work, clean structure, and insightful discussion that helps researchers and practitioners alike understand how classical and deep methods stack up on facial-expression recognition.