Add age-split run analysis for faceset_001
Documents the 2026-04-26 split of faceset_001 (707 curated faces) into 6 substantive era buckets + 68 thin fragments, including the readiness probe evidence, the anchor-based assignment rationale (replaces transitive union-find that caused year-drift), and the re-run / apply- to-other-identity workflow. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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docs/analysis/age-split-faceset-001.md
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# Age-splitting faceset_001 into era-specific facesets
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_Run date: 2026-04-26. Cache: `work/cache/nl_full.npz` (5260 face records). Source: `work/age_split_001.py` and `work/check_faceset001_age.py`._
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## 1. Why split
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`faceset_001` aggregates a single identity across roughly 20 years of source
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material. The averaged embedding consumed by roop-unleashed therefore mixes
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features from very different ages. For face-swap output that should target a
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specific period (e.g. "this person around 2011" or "this person around
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2018–19"), the identity needs to be split *after* clustering — the cluster is
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correctly one identity, but the averaged embedding is the problem.
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## 2. Evidence the identity is age-sortable
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`work/check_faceset001_age.py` probes `faceset_001` (707 curated faces).
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**Pairwise cos-distance histogram** (249,571 pairs):
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| range | pairs |
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|-------------|------:|
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| [0.0, 0.2) | 1,250 |
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| [0.2, 0.3) | 11,277 |
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| [0.3, 0.4) | 63,920 |
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| [0.4, 0.5) | 92,555 |
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| [0.5, 0.6) | 63,288 |
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| [0.6, 0.7) | 16,048 |
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| [0.7, 0.8) | 1,217 |
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| [0.8, 1.0) | 16 |
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Mean 0.453, median 0.452, max 0.842. The cluster is internally diffuse — wide
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enough to admit non-trivial sub-structure without crossing the
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inter-identity boundary (which sits well above 0.6 in this dataset).
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**Sub-clusters at threshold 0.35** (precomputed cos-dist, average linkage):
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156 sub-clusters, 10 with ≥ 10 faces, top-5 sizes [120, 105, 47, 40, 24].
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The top sub-clusters align with distinct EXIF year medians (2011, 2019,
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2018, 2011, 2010), so the split is meaningful.
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## 3. Pipeline
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`work/age_split_001.py`:
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1. **Seed centroid.** Load the 707 face keys from
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`facesets_swap_ready/faceset_001/manifest.json`; resolve to cache rows;
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normalize the mean embedding.
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2. **Wide recovery.** Pull every face record under `/mnt/x/src/{nl,
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lzbkp_red}` from the cache with cos-dist ≤ 0.55 from the seed. The seed
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is curated and tight, so 0.55 is a safe outer envelope. Result: 1,501
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faces from 4,756 candidates.
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3. **Quality gate** (mirrors export-swap defaults): `face_short ≥ 100`,
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`blur ≥ 40.0`, `det_score ≥ 0.6`. Result: 892 → 856 after one
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re-centroid + tighten pass at 0.50 to absorb the recovery without
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drift.
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4. **Sub-cluster** the survivors at cos-dist 0.35 (precomputed agglomerative,
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average linkage). 223 raw sub-clusters; sizes top-10 = [127, 97, 55, 42,
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40, 25, 17, 14, 13, 11].
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5. **EXIF year per source path.** Read `DateTimeOriginal` once per unique
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path; cache on disk at `work/cache/age_split_exif.json` so re-runs after
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parameter tweaks skip the slow Windows-mount EXIF read. 728 of 855 paths
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were dated.
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6. **Anchor-based fragment assignment** (replaces transitive union-find merge
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that caused observable year drift):
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- sub-clusters with ≥ 20 faces are *anchors* (6 found: dom-years 2011,
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2019, 2018, 2011, 2016, 2010);
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- smaller fragments attach to the single nearest anchor *only if* both
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`cent_dist ≤ 0.40` AND `|dom_year_anchor − dom_year_fragment| ≤ 5`;
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- anchors do not merge with each other (transitive merging produced
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anchor-to-anchor year drift across 2010 → 2014 → 2018 in earlier
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runs);
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- fragments with no qualifying anchor remain standalone.
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7. **Per-era export.** Composite-quality rank, single-face square PNG crops
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(`pad_ratio=0.5`, `out_size=512`), top-N + `_all` `.fsz` bundles, per-era
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`manifest.json`, `<label>.txt` marker, `THIN.txt` for buckets < 20 faces.
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8. **Top-level manifest merge.** New entries are appended to
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`facesets_swap_ready/manifest.json`. Operationally the THIN buckets are
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then moved into `_thin/` and partitioned into a `thin_eras` array (with
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`relpath: _thin/<name>`) so consumers reading `facesets` see only the
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substantive entries.
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## 4. Result
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74 era buckets emitted; 6 substantive + 68 thin/standalone fragments.
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| era | faces | dom year(s) |
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|-------------------|------:|-------------|
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| `faceset_001_2010-13` | 282 | 2011 |
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| `faceset_001_2018-20` | 129 | 2019 |
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| `faceset_001_2014-17` | 125 | 2018 (anchor sub 15 dom_year=2018) |
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| `faceset_001_2018-19` | 107 | 2018 |
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| `faceset_001_2005-10` | 88 | 2010 |
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| `faceset_001_2011` | 43 | 2011 |
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Two distinct 2011 anchors and two 2018-area anchors persist by design —
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embedding-space distance separated them despite year overlap. The era-label
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collisions are disambiguated with `_v2` suffixes, but only when both anchors
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landed on the *same* literal label string (none of the substantive six did).
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The 68 thin buckets are largely 1- or 2-face fragments with idiosyncratic
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embeddings; they are quarantined into `_thin/` rather than deleted because
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some are legitimate edge poses / lighting / age extremes that may be useful
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for narrow targeted swaps.
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## 5. Re-running and applying to other identities
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- **Re-run with different parameters**: just re-execute `age_split_001.py`.
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Embeddings are loaded from cache, EXIF is loaded from
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`age_split_exif.json`, and only the sub-cluster + export steps re-run.
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Total runtime ~2 min.
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- **Apply to a different identity**: copy `age_split_001.py` to
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`age_split_NNN.py` and change `FS001`. The `SCAN_ROOTS`,
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`RECOVERY_THRESHOLD`, `TIGHTEN_THRESHOLD`, `SUBCLUSTER_THRESHOLD`,
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`ANCHOR_MIN_SIZE`, `FRAGMENT_CENTROID_MAX`, and `FRAGMENT_YEAR_MAX`
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defaults are tuned for `faceset_001`'s ~707-face curated cluster; smaller
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identities likely need lower `ANCHOR_MIN_SIZE`.
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- **Always quarantine THIN buckets** afterwards using the same partition
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pattern (move to `_thin/`, split top-level manifest into
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`facesets` + `thin_eras`). The script appends THIN entries to the top-level
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manifest as if they were full facesets, so the cleanup is a separate step.
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