Bookface Reaches the Sample-Image Testing Stage
Bookface has moved from concept into user-testing readiness. The system is waiting on Brett to provide sample images, which is exactly the right next gate for a privacy-first face-matching feature.
The important point: Bookface is being built as local/on-site infrastructure for Gallery auto-tagging first, not as a broad surveillance feature.
The mission scope was deliberately conservative. The first target is Discord Gallery auto-tagging, with management-only face-tag metadata and smart album support for staff and participants. Safe Arrival and CCTV remain planning-only unless separately approved.
That boundary matters. Face matching is high-risk if it is treated casually, especially in a care/admin environment. The mission rules are clear: no cloud facial recognition API, no off-site biometric processing, no logging of embeddings or sensitive biometric arrays, and false positives are blocker-level. The system should prefer unknown or uncertain over forcing a nearest match.
What Was Established
The discovery pass mapped the existing gallery and media surfaces, including gallery routes, admin-drive serving, auth, media grabber behavior, frontend gallery files, and the current media schema. It also confirmed existing media item keys, thumbnail behavior, and the current gallery API shape.
The Bookface direction then moved into reference-library scanning and local runtime readiness. The plan uses the existing Gallery and Media Grabber workflows rather than replacing them. Identity folders are designed to stay human-sortable and machine-matchable, with staff and participant identifiers preserved in the folder naming convention.
The model/runtime side was also investigated. The work stayed anchored to local model files on Lucas, not the repo or NAS. sharp was already present, onnxruntime-node required explicit approval, and the CUDA/provider investigation was treated as a readiness task rather than a hidden dependency change.
Why Sample Images Are the Next Gate
The next meaningful test needs real sample images. Synthetic confidence is not enough here. A face-matching system has to be calibrated against the kind of images RABS will actually see: staff and participant reference samples, gallery photos, uncertain faces, low-quality captures, group photos, and cases where the right answer is "unknown."
That is why the system is waiting for Brett. The technical path can be ready, but acceptance depends on practical calibration and false-positive behavior.
What This Enables
Once tested, Bookface can make Gallery more useful without making media handling heavier. Staff and participant smart albums can become easier to maintain. Historical Discord media can become more searchable. Management can review face-tag metadata without exposing biometric machinery through the normal user surface.
The next step is not more theory. It is sample-image testing, calibration, and a careful decision about what level of confidence is acceptable before anything is treated as live.