Face Recognition
Identifying a person from their face — technically solved, and the single clearest case of a system that works well on average and fails on specific people.
Reviewed July 14, 2026Stable
When not to use it
- 1:N identification with a large gallery. False positives scale with N. The quoted accuracy is the 1:1 number.
- Where a false positive means arrest. The error distribution falls hardest on people already over-policed, and it has happened.
- Without disaggregated evaluation. An aggregate rate averaged over a population that doesn't experience the system equally describes nobody.
- On anyone who didn't consent. For verification, consent is inherent. For surveillance it's impossible.
Reach for something else instead
- Other biometrics — fingerprint, iris. Require cooperation, which is the point.
- Non-biometric identity — badges, cards, passwords. Revocable, which faces aren't.
- Human review as a gate — with the rubber-stamp caveat.
- Not identifying people — a lot of stated use cases don't actually require knowing who someone is.
Further reading
- Buolamwini & Gebru (2018), Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification — 0.8% vs 34.7%. The paper that changed the field.
- Grother, Ngan & Hanaoka (2019), NIST FRVT Part 3: Demographic Effects — 189 algorithms, 99 developers. The independent confirmation at scale.
- Schroff, Kalenichenko & Philbin (2015), FaceNet: A Unified Embedding for Face Recognition and Clustering — the architecture everything still uses.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Quoting a verification accuracy number in an argument about surveillance. Different problem, different error profile.
- Treating one accuracy number as the accuracy. Gender Shades is exactly what that conceals.
- Assuming more data fixes it entirely. It helps a lot; the single-threshold problem remains.
- Forgetting your face isn't revocable. A leaked password can be changed.
At a glance
FieldComputer Vision
The finding0.8% error for lighter men, 34.7% for darker women (Gender Shades, 2018)
ConfirmedNIST, 189 algorithms, 99 developers
Two problems1:1 verification (safe) vs 1:N identification (false positives scale with N)
The unfixable bitno single threshold is fair to every group
DifficultyBeginner
Often compared with
Verification vs. identification — one asks "are you who you claim" with your consent and one comparison; the other asks "who are you" against a million strangers. Only the first is nearly solved.
Where this sits
A destination. 10 concepts lead here, and nothing in the corpus depends on it.
7Levelsteps in
10Needs firstconcepts
0Opens upnothing further
1Areastays here
Learn these firstImage Classification
Computed from the prerequisite graph, not assigned. How this works