study(VR-012): quality knee over a joint size x blur grid, three blur families

Extends the VR-005 protocol -- hold out one mugshot per actor, degrade
only the probe, match against a gallery held at native resolution,
decide through the Platt calibration -- from one axis to two, over 1670
actors rather than 100.

Joint rather than separable, because the interaction is the question: a
16 px face upscaled to 112 has already lost its high frequencies, so
further blur costs it almost nothing, while the same blur at full
resolution is expensive. Sweeping the axes independently would measure
each with the other implicitly at its best and miss that entirely.

Three blur families, compared at matched per-axis PSF spread rather than
at equal raw parameter. Optical defocus is a uniform disc whose transfer
function is a jinc with exact zeros, not a Gaussian that merely rolls
off, and it is also how a face ends up large and useless -- the case a
size filter cannot catch. Sweeping Gaussian alone, as the first version
did, understates real lens blur by a factor of five in error rate.

Every candidate measure is scored on every degraded crop and the
candidates are ranked by how well each predicts the pipeline's actual
decision, not by how smooth its synthetic ladder looks. Both a pooled
and a within-cell AUC are reported: they answer different questions and
the candidates rank differently under each.

Runs through sae_embed throughout. Stages gains optional engine paths so
the same study can drive a TRT build, which is what makes the full grid
five minutes rather than four and a half hours.

TRACES: VR-012, AR-028, AR-029 | SR-002

scripts/validation/min_face_size.py

@@ -183,10 +183,16 @@ DEDUP_SIM = 1.0 - 1e-7
 class Stages:
     """Thin holder so the rest of the script has one object to call."""
 
-    def __init__(self, detector: str, arcface: str, conf: float, nms: float):
+    def __init__(self, detector: str, arcface: str, conf: float, nms: float,
+                 detector_engine: str = "", arcface_engine: str = ""):
+        # The engine paths are only consulted by a TRT-backend build, where they
+        # are mandatory — that backend loads a pre-built .engine and will not
+        # fall back to reading the .onnx. An ORT build ignores them, so passing
+        # them unconditionally is safe and keeps one constructor for both.
         self.engine = sae_embed.FaceEmbedder(
             detector_model=detector, arcface_model=arcface,
-            conf=conf, nms=nms, max_side=0)
+            conf=conf, nms=nms, max_side=0,
+            detector_engine=detector_engine, arcface_engine=arcface_engine)
 
     def detect(self, img):
         return self.engine.detect(img)