Age Estimate

Face Age

Estimate your perceived facial age in about a minute.

EU servers60 secondsNot storedNo account needed

By starting, you consent to processing your photo and heart-rate data. Read the privacy policy

Not a medical device. Results are for informational purposes only and don't constitute medical advice. Read the full disclaimer

The basics

Face age vs. biological age vs. chronological age

Three numbers, three meanings.

Chronological age
How many years since you were born.
Biological age
How old your body is, measured by epigenetic, metabolic, or functional markers. Requires lab work.
Face age
How old you appear, estimated from visible facial features. Correlates with biological age but isn't a clinical measurement.

Face age is the most accessible of the three: it takes 60 seconds, no blood draw, no waiting period. It's a starting point, not a diagnosis.

How it works

More accurate in real conditions

Most age estimators use shallow CNNs trained on tight face crops. Our dual-stream transformer reads face and body context together — so it works on real photos, not just headshots.

What influences it

Lifestyle factors that show up on your face

  • Sleep quality and duration
  • UV exposure and sun protection
  • Smoking and alcohol intake
  • Stress and cortisol patterns
  • Nutritional status (especially antioxidants, omega-3s)
  • Cardiovascular fitness (skin perfusion)
  • HRV and recovery
  • Hydration and water balance

These are also exactly what our Lifestyle Test measures.

Common questions

Common questions about face age

How accurate is AI face age estimation?

Our model uses face and body context to estimate perceived facial age, which correlates with biological age but isn't a clinical measurement. Accuracy varies with lighting, expression, and photo quality. For clinical biological age, you need lab work like an epigenetic clock.

What's the difference between face age and biological age?

Face age is how old you appear from visible features (skin, structure, posture). Biological age is how old your body is at a cellular level, measured with lab markers. Face age is a quick proxy; biological age is the underlying truth.

Can my face age change?

Yes. Sleep, sun protection, smoking, alcohol, stress, nutrition, and cardiovascular fitness all show up on your face over time. Sustained lifestyle changes typically take 4–12 weeks before they're measurably visible.

Why is my face age different from my real age?

A few years off your chronological age is normal — most people don't look exactly their age. A larger gap can reflect lifestyle factors, genetics, or photo conditions. Try the test a few times in different lighting before drawing conclusions.

Does lighting affect my result?

Yes. Natural daylight gives the most accurate result. Harsh shadows, low light, and yellow indoor lighting can make you look older. Face a window for the best result.

Is my photo stored?

No. Your photo is sent to our EU servers for age estimation and discarded immediately after. Heart rate and HRV measurements run entirely in your browser and never leave your device.

Can I take the test more than once?

Yes — take it as often as you like. Results vary day to day with sleep, stress, and lighting. We recommend tracking the trend over weeks rather than acting on a single result.

How is this different from a blood-based biological age test?

Blood-based tests like epigenetic clocks (Horvath, DunedinPACE) measure biological age at the molecular level — much more precise but slow and expensive. Face age is an instant proxy: less precise, but free and easy to track over time. Use both if you can.

References

  1. Bontempi et al. (2025). FaceAge, a deep learning system to estimate biological age from face photographs. The Lancet Digital Health.
  2. Haugg et al. (2026). Face aging rate quantifies change in biological age to predict cancer outcomes. Nature Communications.
  3. Kuprashevich & Tolstykh (2023). MiVOLO: Multi-input Transformer for Age and Gender Estimation.arXiv:2307.04616
  4. Kuprashevich, Alekseenko & Tolstykh (2024). Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation.arXiv:2403.02302