Age Estimation
Overview
Estimating age from facial imagery is technically difficult and operationally sensitive. In investigative contexts, accuracy, bias control, and auditability matter.
Rigr AI's Age Estimation capability provides highly accurate age estimates from facial images and video frames and is designed specifically for law-enforcement and regulated use.
Model Performance
The current model (v3) is a vision transformer fine-tuned on a large, curated dataset of real-world images with verified age labels. It produces a point estimate along with a calibrated uncertainty value for each detected face.
Accuracy by age range
| Age range | MAE (years) | Within ±1 year | Within ±2 years |
|---|---|---|---|
| 0–2 | 0.41 | 95.3% | 97.7% |
| 2–5 | 0.72 | 88.3% | 97.0% |
| 5–8 | 0.77 | 86.1% | 96.4% |
| 8–13 | 1.06 | 73.6% | 93.1% |
| 13–18 | 1.26 | 69.9% | 85.0% |
| 0–18 (all minors) | 0.87 | 81.9% | 94.1% |
| 18–26 | 2.11 | 53.9% | 71.7% |
| 26–40 | 3.30 | 36.8% | 52.7% |
| 40+ | 5.04 | 22.4% | 34.2% |
Competitive performance
Independent evaluation against leading commercial age estimation systems shows that Rigr AI outperforms competitors at every single age from 0 to 16, with particularly strong advantages for younger children (ages 0–5) and the pre-teen/early-teen range (ages 8–15) where competitor error rates are 2–3× higher.
Calibrated uncertainty
Every prediction includes a calibrated uncertainty value (± years) representing a 1σ confidence bound. The model's uncertainty estimates are rigorously validated: at the 1σ level, actual coverage is 68.48% against a theoretical ideal of 68.27% — near-perfect calibration. This means the reported confidence intervals are trustworthy and actionable.
Try It
Take a photo or upload an image to see age estimation in action.
Processed securely — no images are stored.
Operational Use
Within VST Teams, Age Estimation is used to:
- Highlight potentially sensitive content involving minors
- Prioritise review across large media datasets
- Support evidential assessment without replacing human judgement
The capability is also available as a standalone API or lightweight application that can be deployed in an air-gapped environment. Plug-ins are available for major forensic image analysis tools.
Integration with Griffeye
Age estimation results surface directly inside Griffeye Analyze, letting investigators filter and prioritise without leaving their existing workflow.
Evidence at Scale
Age estimation enables investigators to rapidly characterise large volumes of seized material. The chart below shows the estimated age distribution across 100,000 faces detected in child sexual abuse material — illustrating how the capability can summarise large evidence sets for investigation and court presentation.
Deployment and Control
- Fully containerised
- On-premise and air-gapped operation
- No data retention
- Customer retains full control of inputs and outputs