Explainable Multi-Face Detection System for Large-Scale Crowds During Examinations

An explainable multi-face detection system for large-scale crowds during examinations.

An explainable multi-face detection system for large-scale crowds during examinations. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are explanations of each result (feature importance or visual evidence), dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses SHAP, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.