---
title: "3.8 Confidence Scores & Hybrid Fusion"
description: "PupilMetrics runs ****two independent analysis pipelines**** on every image and then fuses their outputs into a single confidence score. ****Classical CV (pixel-based)**** The classical computer vision pipeline uses circle detection, radial sampling, and boundary-point analysis on the full-resolution image. It…"
url: "https://cnri.edu/docs/3-8-confidence-scores-hybrid-fusion/"
updated: "2026-03-24"
category: Reading the Analysis Results
---

# 3.8 Confidence Scores & Hybrid Fusion

PupilMetrics runs ****two independent analysis pipelines**** on every image and then fuses their outputs into a single confidence score.

****Classical CV (pixel-based)****

The classical computer vision pipeline uses circle detection, radial sampling, and boundary-point analysis on the full-resolution image. It produces pixel-accurate iris and pupil boundaries.

****ML Model (ONNX)****

The machine learning model (`cnri_model.onnx`) is a neural network trained on iris images, resized to a normalized 224×224 crop centered on the detected iris. It outputs four regression values: PI ratio, decentration, ellipseness, and decentration angle.

****Hybrid Confidence Formula****

The four components are weighted and combined:

| Component | Weight | What it measures |

|-----------|--------|-----------------|

| Capture quality | 20% | Image sharpness, brightness, contrast from the quality gate |

| Classical CV confidence | 35% | Circle detection score from the Hough-like iris finder |

| ML plausibility | 20% | Whether the ML outputs fall within anatomically reasonable ranges |

| Cross-model agreement | 25% | How closely the two pipelines agree on PI ratio (80%), ellipseness (10%), and decentration (10%) |

The fused confidence is displayed as a percentage and maps to the familiar grade:

| Fused confidence | Grade |

|-----------------|-------|

| > 75% | ****A**** |

| 60–75% | ****B**** |

| 45–60% | ****C**** |

| < 45% | ****D**** |

****Safety caps****

If classical CV confidence falls below 25%, or if capture quality falls below 30%, the fused score is capped at 40% or 50% respectively, regardless of other components. This ensures a poor underlying image always produces a conservative grade.

****When the two pipelines disagree****

When the classical and ML results differ significantly on PI ratio (> 10% tolerance), the agreement component reduces the hybrid score. The individual classical and ML values are still shown in the results for reference when "Show ML Comparison" is enabled in settings.