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.
Age estimation isn't only a law-enforcement tool. Trust & Safety teams use the same calibrated point estimate and uncertainty to route age-ambiguous AI-generated and user-uploaded content for review. See the Trust & Safety solution page →
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.
From a VST Teams run across 100,000 faces in seized CSAM.
Try It
Take a photo or upload an image to see age estimation in action.
Images you submit are kept for up to 7 days for trust & safety review, then deleted. Do not upload images of identifiable people without their consent. How we handle demo data →
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.
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.
Deployment and Control
- Fully containerised
- On-premise and air-gapped operation
- No data retention
- Customer retains full control of inputs and outputs
For Developers
The Age Estimation API accepts base64-encoded images and returns estimated ages, bounding boxes, confidence scores, and calibrated uncertainty for every detected face.
POST /api/image curl -X POST https://api.age.rigr.ai/api/image \
-H "Content-Type: application/json" \
-H "X-API-KEY: $API_KEY" \
-d '{"images": ["'$(base64 -w0 photo.jpg)'"}'{
"results": [{
"results": [{
"age": 25.3,
"uncertainty": 1.2,
"bbox": [175, 133, 364, 378],
"score": 0.9998
}]
}]
}Independent evaluation
Rigr AI took part in the Australian Age Assurance Technology Trial (2025) — an independent evaluation commissioned by the Australian Government and conducted by the Age Check Certification Scheme, which tested participants against a dataset the trial built rather than against each vendor's own benchmark.
Its assessment found Rigr AI strong on technical readiness, privacy alignment and usability, delivering reliable threshold-based age decisions with fast on-device processing and low false-positive rates in real-world scenarios. It noted the privacy-centric design — no personal data stored, biometric inputs processed and deleted locally in milliseconds after estimation — and the configurable buffer zones that let a relying party manage uncertainty rather than depend on a single hard threshold. The individual test report and participant interview are published on the trial's site.
Mean-absolute-error figures are not comparable between evaluations run on different datasets. Our own benchmark figures and any result measured on the trial's dataset are different tests, and a lower number on an easier set is not a better model.
Integrating it yourself?
The full Age Estimation API reference covers every endpoint, the request and response schemas, error codes, image limits and self-hosted Docker deployment — or get a free API key and start calling it. For review-routing workflows on a platform, see Trust & Safety age estimation.
Frequently asked questions
How accurate is Rigr AI's age estimation?
The current model achieves an overall mean absolute error of 1.64 years, falling to 0.87 years for minors, with 68.9% of estimates within one year of the true age. Every estimate carries a calibrated uncertainty value.
Can age estimation run on-premise or air-gapped?
Yes. It is fully containerised and runs on-premise or air-gapped with no external connectivity and no data retention; inputs and outputs remain under your control.
How does it scale to a full case?
Within VST Teams, Age Estimation runs across thousands of files in a case and returns the age distribution, the identified minors, and a priority queue. It is also available as a standalone API or lightweight application.
Does it replace human judgement?
No. It supports triage and evidential assessment; a calibrated uncertainty accompanies every estimate so reviewers retain final judgement.
Has it been independently evaluated?
Yes. Rigr AI took part in the Australian Age Assurance Technology Trial (2025), an independent evaluation commissioned by the Australian Government and run by the Age Check Certification Scheme against a dataset the trial built. Its assessment found Rigr strong on technical readiness, privacy alignment and usability, with reliable threshold-based decisions, fast on-device processing and low false-positive rates in real-world scenarios. The individual test report is published on the trial's site.