How to deploy face recognition across an enterprise: watchlists and alerts, enrolment quality, liveness checks, consent and erasure, and privacy rules done right.
Face recognition has moved on from simple picture matching. Modern systems turn each face into a compact digital signature that stays recognisable across changes in lighting, angle and expression, then compare it against a list of people you have enrolled.
In Visylix, face recognition supports watchlists and alerts for unknown people. When a camera sees someone on a watchlist, Visylix raises an alert for an operator to confirm, with the live feed and the reference photo side by side. It handles several faces in one frame, searches large galleries quickly, and you set the match threshold to suit each deployment. Like all Visylix AI, it runs entirely on your own hardware.
Results depend on the quality of the photos you enrol. Use several reference photos per person, including a straight on view, slight angles and different lighting. Use clear, well lit, high resolution images, and refresh them as people's appearance changes over the years.
Large organisations can enrol in bulk from HR or badge photo systems through the Visylix API. Every reference photo is scored for quality, so poor images are flagged before they weaken the gallery.
Camera placement matters just as much. Faces need to be seen from the front at a sensible height and distance, which is why entrances, turnstiles and corridors work far better than wide outdoor views.
The main threat to face recognition is a presentation attack: holding up a printed photo, replaying a face on a screen or wearing a mask. Strong systems stop these at the point where it matters most.
Visylix runs a liveness check at enrolment, so printed photos and screen replays are rejected before they ever enter the gallery. On live CCTV, every match goes to an operator to confirm, so a person always makes the final call.
Face recognition is regulated, and rightly so. The EU AI Act prohibits real time remote biometric identification in publicly accessible spaces for law enforcement, outside narrow exceptions, and classes other remote biometric identification as high risk, with assessment and transparency duties. The GDPR generally requires explicit consent for processing biometric data.
Good practice is clear. Use face recognition only for a stated security purpose. Store face templates rather than raw images where possible. Delete data automatically after a defined period. Post clear signage and keep records of what you process and why.
Visylix is built to support this. It records consent at enrolment, supports erasure when a person asks to be removed, keeps a tamper evident audit trail, and holds all face data on your own hardware, never in a vendor cloud.
Results depend mainly on camera placement, lighting and the quality of enrolment photos, so the right test is your own cameras. Visylix lets you set the match threshold per deployment, scores every reference photo for quality and sends every match to an operator to confirm, so decisions stay with a person.
Face recognition is one of 22 AI analytics that you add per camera per month, on the cameras where you need it, alongside any Visylix plan. Starter and Pro include unlimited cameras on the VMS licence, from ₹4,999 or $49 per month. Talk to us for AI pricing.
Visylix checks liveness when a face is enrolled, rejecting printed photos and screen replays, and scores every reference photo for quality before it enters the gallery. On live camera feeds, every match is sent to an operator to confirm.
Face recognition is allowed but regulated. Under the EU AI Act, real time remote biometric identification in publicly accessible spaces for law enforcement is prohibited, with narrow exceptions, and other biometric identification is classed as high risk. The GDPR usually requires explicit consent for biometric data. Store templates rather than raw images, apply retention limits, post clear signage and document the purpose of each deployment.