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.
About Pranjul Rathour

Requirements gathering, on stage
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
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Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: [email protected] · Invite me to your campus
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