Measure the faces already found rather than finding them again

Every face stored before its quality was kept holds a unit vector, and
V14 forgot the run marker of each image holding one so that the next
sweep would look again. Looking again meant detecting again: a whole
re-detection per image, with every suggestion on it thrown away and the
confirmations carried across by box overlap, to recover one number.

The sweep now has a measuring pass between the proxy repair and the
un-indexed images. It lists every image holding an unmeasured face,
fetches the original once, warps each stored face from the landmarks it
already has, embeds it, and writes the raw vector and its length over
the old row. Ids, boxes and identities are untouched; the marker is
re-written fresh so the sync exports the measured vectors. A face whose
landmarks no longer make a warp is dropped, as detection would have
refused to store it. `faces_unindexed` leaves those images to the
measuring pass, so the V14 deletion no longer costs a second detection.

docs/faces.md

@@ -816,8 +816,11 @@ here, and it is the phone and tablet story that should decide whether it gets bu
   anything downstream sees them. A probe's confidence (§9.1) is computed from the references it
   matched; a reference's confidence hears nothing from a probe. A face whose quality was never
   recorded is admitted to the gallery — a rule that cannot be checked admits rather than excludes —
-  and schema V14 forgets the run marker of every image holding one, so the next indexing pass
-  measures it.
+  and the next indexing pass **measures** it: `faces_unmeasured` lists every image holding one, and
+  each such face is embedded again from the native render with the landmarks it already has, the raw
+  vector written over the old one and its id, box and identity untouched
+  (`faces::record_measurements`). No detector runs and no suggestion is lost — the cost is the
+  original fetched once more, since the length exists only at the moment of embedding.
 
 **The algorithm.** Constrained average-link agglomeration over the probability graph, merging while
 the average pairwise probability exceeds **0.9** and no cannot-link is violated. Average-link rather