# Vision & Face Recognition Audit

The Face Recognition system is located in `backend/Face_Recognition`.

## Key Findings:
1. **Matcher Engine**: `FaceMatcher` handles operations via `InsightFace` models. 
2. **Embedding Storage**: Enrollments are saved as `.npy` files. Stored inside `faces_db` and `temp_faces_db`.
3. **Concurrency**: It employs thread locks (`threading.Lock()`) for updating the embedding dict safely across background refreshes and API calls.
4. **Background Refresh**: Boot sequence kicks off a thread to refresh stale embeddings without blocking startup.
5. **Dynamic Thresholds**: Reads defaults via env vars (`FACE_MATCH_THRESHOLD`) but overrides them based on a `PERSON_THRESHOLDS` mapping for predefined demo identities (mk, urvi, vidit), ensuring fewer false positives.

## Recommendations:
- Transition from file-based `.npy` lookups to a vector database if the identity count exceeds a few thousand.
- Ensure the background thread handles InsightFace GPU resource contention if any.