A face-matching system doesn't return 'yes' or 'no' — it returns a similarity score, and a threshold turns that score into a decision. Every threshold choice trades one error type for the other.
The two failure modes
- False accept: the system matches two different people. A low threshold makes this more likely — bad for a security use case.
- False reject: the system fails to match the same person to themselves (different lighting, angle, glasses). A high threshold makes this more likely — bad for user experience.
How to actually pick the threshold
Decide which error your use case can tolerate less. A campus entry gate probably tolerates occasional false rejects (someone re-swipes) far better than a false accept (a stranger gets in). Test the threshold against a labelled dataset that includes hard cases — different lighting, angles, ageing — not just clean enrolment photos.
See liveness detection and anti-spoofing basics for the layer that sits alongside matching in a real access-control system.
— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.