Face Recognition powered by AI
Used for facial recognition in a stream of people while determining gender, age, race, and ethnicity of a person
Advantages of face recognition
Up to 99.35% facial recognition accuracy
Up to 200 simultaneously recognized faces in the frame at requested resolution
More than 100 million recognized faces for 1 client within 1 month period of time
- 01 Search and facial recognition in real time
- 02 View location and movement of a person with instant notification that such person has been found
- 03 Face identification and finding a match against a database of faces
- 04 Detection of additional information: gender, age, race, emotions with unbiased technology
- 05 Face identification with additional characteristics like beard, mustache, glasses, face shield, mask
- 06 Creating lists of people whose movement should be controlled
- 07 Creating White/Black & VIP Lists
- 08 Supports database import (CSV) and export (EXCEL/PDF/CSV/JSON)
- 09 Ability to process statistical data and generate reports
- 10 Business logic constructor
- 11 Cross-platform and multilingual interface
Why choose our AI face recognition software?
This is not vaporware — it’s practical technology engineered for real operations. Drawing from our experience, organizations pick face recognition for three simple reasons: faster response, frictionless access, and richer operational data. Want fewer queues at gates? Faster threat detection? Better attendance tracking? We’ve built for that.
- Up to 99.35% facial recognition accuracy on calibrated camera setups (day/night, good angle). As indicated by our tests, accuracy depends on camera placement and lighting.
- Up to 200 simultaneously recognized faces per frame at requested resolution — useful for busy lobbies and transit hubs.
- High-volume capacity: more than 100 million recognized-face events for one client in a month in large deployments (analytics/archival workflows).
- Real-time search & match: instant lookup against VIP, blacklist, or staff lists.
- Accessory & appearance detection: beard, mustache, glasses, face shield, mask, plus age-range estimation and non-identifying demographic summaries (aggregated only).
- Export & integrations: CSV / Excel / PDF / JSON exports; MySQL, PostgreSQL, MS SQL, Oracle support.
- Business logic constructor & RBAC: build alerts and pipelines, fine-grained user rights, and audit trails.
- Cross-platform, multilingual UI for global teams.
How it works — simple, effective pipeline
01
Capture: IP / ONVIF cameras stream to edge nodes or directly to our service. Metadata (time, camera ID, zone) is attached.
02
Detect & encode: faces are detected in each frame; embeddings (vector representations) are generated.
03
Match & decision: embeddings are matched to on-prem or cloud indexes using configurable thresholds, then business logic runs (alerts, access, billing).
04
Action & audit: real-time notifications, gate control, attendance logs, reports — all with encrypted storage and audit trails.
Real products & tools we work with
We integrate with common camera and compute ecosystems so you don’t have a forklift upgrade:
- Cameras: Axis, Hikvision, Dahua (ONVIF/RTSP).
- Edge hardware: NVIDIA Jetson family, Intel NUC, dedicated GPU servers.
- Cloud & APIs: Microsoft Azure Face API, Amazon Rekognition and open frameworks (OpenCV, dlib, FaceNet, PyTorch models).
VMS & Access: Milestone, Genetec and custom door access systems.
Automatic License Plate Recognition uses computer vision and AI license plate recognition models to identify plates in video or images, decode characters, and link them to useful context (time, location, make/model/color, direction, lane). In short, it turns raw camera feeds into actionable vehicle intelligence for parking management, safe city, retail loss prevention, logistics, and access control.
As per our expertise, a modern license plate recognition program typically includes:
- An edge or server-side inference engine (GPU/CPU/ASIC like NVIDIA Jetson, Intel OpenVINO).
- A license plate recognition camera (or any RTSP/ONVIF stream).
- A backend with storage, search, and license plate recognition online dashboard.
- APIs/SDKs to integrate with gates, VMS, parking payment, or ERP/CRM.
Use cases — where face recognition pays back fast
Alerts for missing persons, suspect re-identification across cameras, and faster situational awareness for control rooms. When we trialed this product in municipal pilots, response times to critical alerts improved significantly thanks to real-time routing of video and notifications.
Identify loyalty members as they enter and instantly push tailored offers, accelerate checkout for recognized customers, and strengthen loss-prevention workflows to improve overall retail efficiency.
Enhance industrial safety and efficiency with face recognition systems that control access to restricted zones, monitor compliance with safety protocols, and prevent unauthorized entry. Manufacturers reduce downtime and improve workforce accountability by integrating with existing security and ERP systems.
Replace manual sign-ins and keycards with a face recognition attendance system for offices, schools, hospitals and factories. We’ve seen factories reduce entry-line times and simplify payroll reconciliation by integrating with HR systems
Passenger flow analytics, queue measurement, and authorized-staff access to secure zones. Airport face recognition systems must be privacy-aware — we provide aggregated analytics and minimal retention options.
Frictionless entry for residents and vetted visitors, plus audit logs for property managers. Based on our observations, residents value the convenience when privacy controls are clear.
Deployment choices: Cloud vs Edge vs Hybrid (quick table)
Option |
Best for |
Pros |
Cons |
|---|---|---|---|
|
Edge |
Low-latency gates, privacy-sensitive sites |
Low bandwidth, faster alerts, data stays local |
Higher hardware cost, local maintenance |
|
Cloud |
Centralized analytics, multi-site correlation |
Easy scaling, centralized updates |
Bandwidth, possible higher latency |
|
Hybrid |
Large deployments with many sites |
Best of both: local inference + central index |
More complex architecture |
FAQs
A biometric face recognition system extracts facial features into a numeric embedding and compares those embeddings to identify or re-identify people, while a standard camera only records images.
Accuracy varies by camera, angle, lighting and thresholds — in a calibrated deployment we’ve seen up to 99.35% for single-face reads. Multi-face scenes and occlusions reduce raw accuracy, which is why we combine multi-frame voting and business rules.
Yes — we support integrations with VMS, door access systems (card readers/locks), and HR/payroll systems to enable attendance system face recognition and face recognition door access system workflows.
Yes — we’ve deployed face recognition attendance systems for factories and hospitals. We recommend edge inference for low-latency gate flows and careful privacy controls in medical contexts.
We run accuracy audits across demographic slices during POC and adjust training/thresholds, use multi-dataset validation, and deploy conservative fail-safe defaults. We also avoid inferring protected attributes and instead report aggregated, anonymized insights.