# xsource — cross-source identification probe (VR-013)

Gallery from **one** recording, probes from **another**, swept over the probe's
input resolution. Complements VR-005, which asked the same question over gallery
mugshots: that one degrades an already-aligned 112×112 crop, holding alignment
perfect, so it isolates the embedder. This one downscales the **whole frame**
before the detector, so detection and landmark regression degrade with it.

Corpus: two Pexels clips of one shoot (4096×2160, 25 fps), four people, all four
present in both. Clips and hand-sorted crops are gitignored — push them with
`scripts/artifacts/push_artifacts.sh`, because the sorting is human ground truth
and expensive to redo.

## Scripts

| script | does |
|---|---|
| `dump_faces.py` | detect every face, write a context crop per detection + a manifest |
| `redraw_boxes.py` | redraw those crops with the detection boxed, in place |
| `propose_labels.py` | propose labels for one clip from another clip's hand-sorted folders |
| `make_review_site.py` | local `review.html` — current label, crop, better match, correct and export |
| `apply_corrections.py` | apply the exported `corrections.json` |
| `verify_labels.py` | integrity gate: index consistency, duplicates, separation. Exits non-zero on failure |
| `resolution_sweep.py` | the VR-013 measurement |
| `failure_analysis.py` | what explains the misses — pose, size, blur, detector confidence |
| `landmark_voting.py` | average SCRFD's overlapping detections instead of discarding them |
| `pose_label.py` | mesh-estimated head pose, for hand correction (feeds VR-012) |

Everything drives the shipped C++ through `sae_embed`; nothing reimplements
detection, alignment, the embedder or the calibration. Scoring goes through the
production gallery sigmoid — never a raw cosine (AR-024).

    LD_PRELOAD=/usr/lib/libcudnn_cnn.so.9 python3 resolution_sweep.py

The preload is needed while ORT's CUDA provider looks for
`cudnnGetConvolutionBackwardDataAlgorithm_v7`, which cuDNN 9 moved into
`libcudnn_cnn.so.9` behind a dispatch stub. Without it everything silently falls
back to CPU.

## What it found

**Resolution is not the binding constraint here.** TPI holds ~41–47% from 4096×2160
down to ~45 px faces, then falls: 23 px → 26%, 18 px → 12%, 14 px → 1.5%. Holding
90% of the plateau needs roughly 50 px end to end, against VR-005's ~22 px — the
gap is detection and landmark error, which VR-005 excludes by construction.

**FPI is 0.0% at every scale.** Resolution loss goes entirely to TBI: the pipeline
stops naming people rather than naming the wrong one.

**The ceiling is cross-view, not resolution.** Every person matches themselves
strongly *within* a recording (sim 0.55–0.85) and collapses *across* the two
(0.14–0.45, threshold 0.335). Only the person with frontal **gallery** references
identified reliably, whatever their probe pose — so the lever is gallery pose
coverage (`docs/pose-expansion.md`), not a better landmark model.

**Landmark voting helps.** SCRFD predicts each face from several anchors and NMS
discards all but one, throwing away a median of 3 landmark estimates per face.
Averaging them, weighted by confidence, lifts cross-clip TPI 41% → 49% for one
forward pass and no extra model. A MediaPipe mesh as landmark source went the
other way (41% → 16%): more stable within a recording, but a ring centroid is not
the annotated landmark ArcFace was trained on, and the embedder punishes the
off-distribution crop.

## Reading these numbers

Four identities, 70 probes, one shoot. The ~47% plateau is pose, not resolution —
half these faces are turned away and never clear threshold at any scale, so the
absolute rates say little and the *shape* is the result. Both clips contain all
four people, so there is no out-of-gallery class and the 10×-weighted out-of-cast
misID is **untested** here; holding one identity out of the gallery would fix
that. And the resolution curve is dominated by the single subject whose gallery
references are frontal.