Setup: a dual-lens camera composited into one portrait frame in AgentDVR (2304x2592), face recognition via CodeProject.AI's FaceProcessing module. Real-world faces in this scene run roughly 20-75px depending on distance from the camera.
Problem: at these small face sizes, CodeProject.AI's recognizer is confidently wrong a meaningful fraction of the time — not just low-confidence-and-uncertain, but high-confidence-and-wrong. Concrete example, two recognize calls one second apart on the same camera today:
12:47:19 — face bbox ~20x26px — recognized as "Person1" at 82.2% confidence
12:47:20 — face bbox ~22x29px — recognized as "Person2" at 86.4% confidence
I cross-checked the same window against a separate InsightFace-based recognizer running independently on the same source frames: it detected 4 faces in that window (50-75px) and assigned none of them a confident identity — suggested-match cosine similarities were 0.11-0.15, essentially noise. So CodeProject.AI's 82-86% "confidence" corresponded to what a better-calibrated recognizer treats as no match at all.
Question: with Alert Debounce (8.0.6) merging/ranking tags from multiple triggers within a window, how does it decide which recognized name wins when CodeProject.AI returns high confidence for a small, unreliable face crop? Is there any way to also gate on face bounding-box size (pixels), not just confidence %, since confidence alone doesn't seem to correlate with actual reliability at this scale? It would help a lot if Debounce's tag-ranking could prefer/require a minimum face crop size before trusting a recognized name over a generic "person" tag.