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dtourolleandClaude Opus 5 042e424961 study(VR-005): minimum face size from downscaled gallery mugshots

Holds out one mugshot per actor, degrades that probe to each candidate
face size and matches it against a gallery held at native resolution,
reporting TPI/FPI per size. Replaces AR-002's 66x66 px working estimate
with a measurement. Needs no video and no ground truth beyond the
mugshot cache already on disk.

LVFace-B over 258 actors, 999 gallery embeddings, threshold 0.754:

    px    12    16    20    24    32    40   48+
   TPI   6.6% 46.5% 81.8% 93.4% 98.1% 99.2% 99.2%

FPI is 0.000 at every size — a face too small to identify degrades to
unidentified, never to a wrong name. rank-1 holds at >=99.6% from 24 px
up, so what fails first is the calibrated probability crossing
threshold, not the ranking.

Two limits on reading this. FPI grows with the number of actors
competing, so 258 understates it against a production library. And
detection and alignment run on the native image with only the resulting
112x112 crop degraded, so landmark error at small face sizes is excluded
by construction and the curve is an upper bound — VR-010 measures the
same question end to end, and lands well above these numbers.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-005 | AR-002

2026-07-31 15:17:39 +02:00

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scripts/validation — per-scene actor-presence eval

Validates the pipeline's per-scene "who's on screen" output against external ground truth, offline. Annealing (anneal_sec) means an actor's presence is only defined after the whole file is merged into [start,end] windows, so we cannot score live: process → write the pipeline JSON → sample timepoints → compare predicted vs ground-truth presence sets → micro-sum TP/FP/FN → precision/recall/F1.

Ground-truth sources

Source	Semantics	Fair to a face pipeline?	What it measures
MovieNet-PS	on-screen face presence per shot	yes — like-for-like	recognition accuracy
Amazon X-Ray (Zenodo)	cast-in-scene (incl. off-camera / non-speaking)	no — penalizes by design	coverage ceiling; recall gap = actors we structurally can't see

MovieNet is the honest recognition number.
X-Ray is an upper bound: its recall gap tells you how much presence is off-camera cast a face detector can never reach — not a pipeline error.

X-Ray dataset: Zenodo DOI 10.5281/zenodo.17659734 (CC-BY-4.0). Per movie it ships people.csv, scenes.csv, people_in_scenes.csv.

Usage

# against Amazon X-Ray CSVs for one title
python scripts/validation/sample_eval.py \
    --pred "Scene in a Mall.json" \
    --xray /data/xray/<movie_dir> \
    --gallery gallery_arcface_w600k_r50.json \
    --step 1.0

# against MovieNet-PS for one title
python scripts/validation/sample_eval.py \
    --pred out.json \
    --movienet /data/movienet --split Train_app10 --title tt0032138 \
    --gallery gallery_arcface_w600k_r50.json

Sampling modes

--step S regular grid every S s (default 1.0) — time-weighted headline number.
--random N N uniform-random timepoints (for confidence intervals).
--scene-anchored one timepoint per GT scene midpoint — the literal X-Ray "did I get this scene's cast right?" question; neutralizes long-scene bias.

Ground truth is compared raw (annealing is not applied to GT).

Matching & masking

Identity is provider-agnostic (identity.py): each actor is the set of every key we can derive — imdb:nm…, tmdb:…, jf:…, name:<normalized>. Predicted and GT actors match iff their key-sets intersect, so an output carrying only tmdb/jellyfin ids still joins X-Ray's nm ids via the normalized-name fallback.

Scoring is masked to gallery ∩ GT: a GT actor absent from the gallery is ignored (not an FN), so we measure pipeline accuracy, not gallery coverage. Without --gallery the mask falls back to GT ∩ pred keys. --no-mask disables it.

Exact id join via the tmdb→imdb crosswalk (recommended)

The gallery/pipeline output key actors by TMDB id (no nm…), while X-Ray and MovieNet key on IMDb. They only overlap on the fuzzy name: key by default. Build a cached tmdb→imdb table once and pass it with --crosswalk to turn the name join into an exact id join:

# one-time: resolve every gallery tmdb id via TMDB /person/{id}/external_ids
python scripts/validation/tmdb_imdb_map.py \
    --gallery gallery_arcface_w600k_r50.json \
    --out scripts/validation/tmdb_imdb.json      # TMDB_API_KEY from env/.env

# then score with exact ids
python scripts/validation/sample_eval.py --pred out.json --xray <dir> \
    --gallery gallery_arcface_w600k_r50.json \
    --crosswalk scripts/validation/tmdb_imdb.json

The table caches nulls (tmdb ids TMDB has no IMDb id for) and checkpoints, so a re-run only resolves new ids. TMDB is authoritative for this crosswalk — there is no clean free bulk tmdb_person ↔ nm file, so we query the API once and cache.

Minimum face size (VR-005)

min_face_size.py is a separate, self-contained study: it needs no video and no ground truth, only the gallery mugshot cache. It holds out one image per actor, degrades that probe to each candidate face size and matches it against a gallery held at native resolution, reporting TPI/FPI per size — the measurement that replaces AR-002's 66×66 px estimate.

python scripts/validation/min_face_size.py \
    --images images --gallery gallery_lvface.h5 \
    --arcface models/LVFace-B_Glint360K.onnx \
    --actors 100 --out experiments/results/vr005_min_face_size

FPI grows with the number of actors competing, so a 100-actor run understates it against a library of thousands: read FPI as relative across sizes, not as an absolute rate. Re-run per --arcface model to see whether min_face_px should be one constant or scale with the embedder (GR-004).

Files

sample_eval.py — CLI scorer.
ground_truth.py — XRayGroundTruth, MovieNetGroundTruth loaders.
identity.py — provider-agnostic match keys.
tmdb_imdb_map.py — build/consult the cached tmdb→imdb crosswalk.
min_face_size.py — VR-005 probe-size sweep (see above).
test_sample_eval.py — self-contained tests (python scripts/validation/test_sample_eval.py).

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