--- name: overcast-lineup description: >- Build a persistent local face database out of case media — the mugshot book — then run a suspect photo through it to identify who they are and where else they appear, with cited similarity scores. --- # overcast-lineup Use this skill when the task is "run this person through the database": accumulate every face across the case's clips and images into a local, browsable lineup, then identify a probe photo against it. Use the broad `overcast` skill and `overcast/reference/verbs.md` for exact flags. The whole DB is local — no media leaves the case. ## Prerequisites The lineup is deepface-only (clustering needs face embeddings the tinycloud face path doesn't expose). `cluster` runs the uv-managed visual-DB Python directly (via `OC_VISUAL_DB_PY`), so you do NOT need to bind the `face` provider — leave your profile's `face` binding untouched. Just prepare the Python once and stand up a `face-cluster` index: ```bash overcast doctor --json # confirm uv + visual-db are ready scripts/visual-db-uv.sh --face # install OpenCV/DeepFace (once per machine) overcast case init --json overcast index create people --type face-cluster --local --json ``` ## Workflow 1. Book every case video/image into the lineup — `cluster add` detects, embeds, and assign-or-creates each face into a person (nearest existing person above `--min-similarity`, else a new one): ```bash overcast cluster add ./interview.mp4 --index --json overcast cluster add ./cctv-lobby.mp4 --index --json overcast cluster add ./mugshot.jpg --index --json ``` 2. Open the lineup — a self-contained HTML contact sheet, one row per person: ```bash overcast cluster view --index --json # add --no-open to only write the gallery overcast cluster list --index --json # people + member counts ``` 3. Run a suspect photo through the database. `cluster identify` reports the most similar person (similarity 0–100) or flags the probe as a likely NEW person, and never writes to the DB: ```bash overcast cluster identify ./suspect.jpg --index --json overcast cluster show --index --json # inspect that person's member faces ``` 4. Name known people and record the identification. Labels survive a `recluster`; point the finding's `--ref` at the `cluster identify` record so its match rides into the brief, and ALWAYS leave a `tldr` note — even a no-match sweep — so the brief can say so: ```bash overcast cluster label "Jane Doe" --index --json overcast finding create "identified suspect.jpg as person (Jane Doe) — 91/100, appears in 3 clips" --ref --confidence high --json overcast note "booked clips into the lineup;

distinct people; suspect.jpg matched at 91/100" --tag tldr --json # Wait for the note result before exporting, so the TL;DR is included. overcast brief --export ./lineup.html --json ``` After a large batch of `cluster add`s, run `overcast cluster recluster --index --json` to re-group every stored face; human labels carry forward. ## Output For each identification return: the probe photo, the matched `person-id` (and label if named), the similarity score (0–100), the member clips/images the person appears in with their `record.id` + `media.at`, and the exported lineup path. A probe with no confident match is reported as a likely new person, not forced onto the nearest face. ## Caveats Similarity is 0–100 (percent), not 0–1 — set `--min-similarity` on that scale. The assign-or-create threshold controls how eagerly faces merge: too low over-merges distinct people, too high splits one person across rows — tune it, then `recluster`. Detection is per sampled frame, so one person yields many member faces; that is expected, not duplicate people. Poor lighting, small faces, and heavy angles lower embedding quality — treat a single borderline match as a lead and corroborate with `face --match` or a second clip before calling it.