The embedder's raw output has a length, and the length is a reading of
how recognisable the crop was: a blur, an occlusion or a hard profile
comes out short. Normalising threw it away. A short vector sits near
the middle of the sphere and matches a little of everyone, which is how
one bad crop bridges two people in a grouping pass.
So the length is kept — the store now holds the raw vector, re-normalised
on load, with the length beside it as `faces.quality` — and a face under
MIN_GALLERY_QUALITY (14) is a probe: measured against the gallery and
placed where it fits, but never what another face is measured against.
Two probes are never paired, and a probe is nobody's evidence for a
confidence. The People screen shows the number as "Quality 17.3", dimmed
below the floor.
Faces indexed before this stored unit vectors and have no reading; they
are admitted to the gallery, and schema V14 forgets the run marker of
every image holding one so the next indexing pass measures them. A
peer's unmeasured shard faces are not adopted, or a sync would write
that marker back.
assign's denominator is the identities the user has ruled on, and it
reads Cluster::person to find them. Master's "Let a name hold a group
together" widened what sets that field: a confirmation, a name, or an
ignore, where before it was a confirmation alone.
The behaviour is right either way — a named person is exactly the
identity a suggestion should be discounted against — but the module note
and faces.md §9.1 both said "a confirmation", which is now too narrow.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The number beside a suggestion was the mean calibrated probability
between the face and the rest of its group, which measures the wrong
thing twice. It punishes coverage: a person with two hundred faces over
fifteen years is *meant* to have members a given photograph is
orthogonal to, so a correct suggestion onto a well-photographed person
scored low for being well photographed. And it never asked who else the
face might be — a face matching Anna at 0.95 and nobody else, and one
matching Anna at 0.95 and her sister at 0.93, came out identical, when
the second is the only one worth the user's attention.
dr_face::assign answers both, and multiplies them: the mean of the best
ten calibrated matches into the identity (the old mean, capped, which is
what stops coverage counting against it), times that identity's share of
the evidence against every *named* rival.
Only named people compete, and per person rather than per group. Both
halves of that had to be measured on a real 18,000-face library rather
than reasoned about. Normalising across every group made the number
useless — median suggestion 21%, four in five under half — because
clustering leaves one person spread over many groups, so a face competed
against itself; and keying rivals by group left Catherine competing with
Catherine, median 39%. Per named person: median 99.5%.
Rivals are gathered below the merge threshold, down to even odds: a
named person matching at 0.6 will never be merged into but is exactly
the competition to discount for. That would be a second similarity scan,
the expensive half of regrouping a library, so cluster_scored scans once
at the looser floor and hands the merge engine the subset at or above
the threshold — pair for pair what it would have scanned for itself,
held to that by a test.
Leave-one-out over that library's 2,702 confirmations across 54 named
people: 99.33% of faces placed on the right person against the old
mean's 99.15%, and the number shown for the right person moves from a
median of 90.4% to 99.3%. It errs low — 100% correct wherever it states
80% or more — which is the safe direction, and docs/faces.md §9.1 says
plainly that the low bands are not calibrated.
The example that measures it comes too: this is a claim about a
library's numbers, and nobody should have to take it on faith.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
dtourolle
