Xeqmate facial recognition turns cameras into face detection points: every person who walks past becomes a searchable face read, feeding live monitoring, face alerts and the persons-of-interest file — with permission controls, an audit trail and privacy governance built into the platform from the start.
Facial recognition in video surveillance is the automatic detection and comparison of faces in camera footage. Every detected face becomes a face read, with a photo, date, time and location, which can be searched, compared against registered photos and used to raise alerts when a person of interest is recognised. At Xeqmate, detection runs on the camera itself (when it has AI on board) or on the platform server, from the video of any RTSP/RTMP camera.
In practice, facial answers questions raw video cannot: has this person been here before? When, and through which cameras? Are these two faces the same person? Has the person on the alert list just walked in? The answers land in the Event Center and in the operation notification channels — and, because biometric data is involved, all of it happens under permissions, auditing and controlled retention, as detailed in the responsibility and privacy law section.
Face detections appear live as people pass the cameras — the control room follow-along screen.
Search the history by period, camera and attributes — including by image: upload a photo and find similar reads.
Upload two photos and see the similarity score between the faces — direct support for identity checks in investigations.
Register the photo of a person of interest, pick the cameras and the trigger schedule, and get the notice when they are recognised — over push, email, Telegram and alert groups.
Detection volume per camera and period — the quantitative view of people movement at the monitored points.
Reads can indicate sex, age range, skin tone, mask use and a liveness check — useful filters for narrowing a search.
Facial recognition gains depth when it is tied to a structured file on the people being monitored.
A record with photos, names and aliases, documents, organisations and tags — and face-alert creation straight from the file, pre-filled with the primary face.
A view of the relationships between people, organisations and incidents — the connections a flat list will never show.
People, points of interest and areas of activity on the map — the geographic context of the investigation.
Places and regions linked to each person or organisation, organising where to look and what to monitor.
Privacy laws treat biometric data as a special category: GDPR Article 9 in Europe, LGPD in Brazil, and state statutes such as Illinois BIPA, Texas CUBI and Washington’s HB 1493 in the United States. Using facial recognition takes more than technology: it takes governance.
Facial recognition is a tool for operations with defined accountability — central stations, security companies and public safety — and it does not replace the customer duty to handle biometric data under the privacy law that applies to them.
Two-way sharing with the City of São Paulo municipal network (Brazil) — cameras, plates and facial recognition integrated.
Sending faces and plates to the São Paulo State Government programme (Brazil), with alerts of interest.
Face alerts also reach the customer own system through a signed webhook, and reads can be queried through the external API — facial is one of the modules of the Xeqmate VMS SaaS, alongside LPR and the video analytics.
Not necessarily. Detection can run on the camera itself (models with face AI on board) or on the platform server — in which case any camera with an RTSP/RTMP stream will do, as long as it delivers the face large, frontal and lit. Mounting at eye level, a narrow crossing point and light on the face matter more than the camera brand.
Beyond the face itself, reads can indicate sex, age range, skin tone, mask use and a liveness check. Those attributes work as filters when searching reads.
You upload the face photo, fill in a name or alias, a category and a description, then pick the cameras that trigger and the schedule (days and hours). When the person is recognised, the notification arrives through the configured channels — email, Telegram and up to 5 alert groups — and the event shows up in the Event Center. Alerts expire on the set date (90 days by default) and can be renewed.
It depends on where you operate. In the United States there is no single federal biometric statute, but state laws such as Illinois BIPA, Texas CUBI and Washington HB 1493 require notice and, in several cases, written consent before collecting face templates — with private rights of action in Illinois. Xeqmate supports that governance with permission-based access, an audit trail, customer-controlled retention and 2FA; defining the lawful basis and the purpose is the responsibility of whoever operates the system.
A structured record of the people being monitored: a file with photos, names, documents, organisations and tags; a relationship graph; a person map with points of interest and areas of activity. From the file, a face alert is created pre-filled with the primary face.
Most cases come down to three causes: backlight (the face turns into a silhouette), a camera mounted too high (you only see foreheads and caps), and a face too small in frame. Mount close to eye level, at a narrow crossing point, with light on the face — and use frontal, sharp, recent photos when registering alerts.
30 minutes with our team: we connect one of your cameras live, you watch the face detections arrive in real time, and you leave with the cost for your own scenario.
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