In short
Nine solution pages covering the four environments face recognition is deployed into — access control, airport eGates, border checkpoints and watchlist monitoring — plus the two architectures that decide the rest of the design, on-premise and edge, and three video management system integrations.
By environment
Where the system is deployed
Each page covers the pain points specific to that environment, the workflow including its failure modes, and a readiness checklist you can use before talking to anyone.
Access control
Let enrolled staff, residents and contractors through a door without a card, and keep an auditable record of every decision including the refusals.
For: Corporate security · Factories · Data centres
Airport eGates
Compare the portrait your document reader has already read against the face at the gate, and hand the officer the exceptions rather than the queue.
For: Airport operators · Border agencies · Ground handlers
Border control
Verify travellers at the checkpoint and search authorised identity records, across sites that may have no connectivity between them.
For: Border and immigration authorities · Ministries of interior · International organisations
Watchlist monitoring
Turn a camera estate nobody can watch into a small number of reviewable alerts, each carrying the evidence an operator needs to decide.
For: Retail loss prevention · Stadiums and venues · Campus security
By architecture
Where recognition actually runs
This decision usually comes from a constraint rather than a preference — a regulator, a latency budget, or a network link that cannot be relied on.
On-premise
Run recognition on servers you own, in a room you control, with a data lifecycle you can show an auditor.
For: Regulated industries · Government · Defence
Edge
Process video beside the camera and send only the event, so the door still opens when the link to head office does not.
For: Multi-site retail · Remote facilities · Small sites
By platform
Video management system integrations
Each page is written from the vendor’s own published documentation, names the mechanism, and states honestly what has and has not been validated.
Milestone XProtect
Put face matches in front of the operator inside Smart Client, attached to the camera and the recorded video, instead of in a second application.
Genetec Security Center
Raise a Security Center custom event on a match so existing event-to-action rules decide what happens next.
Network Optix
Send recognition results into Nx as analytics metadata so matches are searchable on the timeline, not only alertable in the moment.
Describing an integration mechanism is not a claim of certification, marketplace listing or partner status with Milestone, Genetec or Network Optix. No such status is asserted anywhere on this site.
Quick reference
Which solution page answers which question
If you know the question you are trying to answer, this table is the shortest route to the page that answers it.
| The question you have | Matching mode | The page |
|---|---|---|
| How do people get through a door without a card? | 1:N identification | Access control |
| How does a gate check a passport photo against a face? | 1:1 verification | Airport eGates |
| How does a checkpoint verify and search, with no link? | Both, separately authorised | Border control |
| How do I get alerts from cameras nobody watches? | Watchlist (1:N against a list) | Watchlist monitoring |
| How do I keep biometric data inside my own network? | Architecture | On-premise |
| How does a small site work with no bandwidth? | Architecture | Edge |
| How do matches reach my VMS operators? | Integration | Milestone, Genetec or Network Optix |
Not sure which applies
Start with the environment, not the product
Describe the place, the people who pass through it and the decision the system has to make. The first response covers capture geometry and the fallback path, which is where the answer actually lives.