Face Recognition — Rudra Video Analytics

Overview

Know Who Is on Your Premises

Our Face Recognition Service uses deep learning — the same neural network technology used by Google, Microsoft, Amazon, and Facebook — to match faces in live video streams against a database in milliseconds. Unlike badge-based or PIN-based access systems, it is entirely contactless, non-invasive, and works automatically without any cooperation from the subject.

Deployed at Mumbai Railway Station using a vehicle-mounted PTZ camera, our system has demonstrated reliable identification in dense, moving crowds under real-world conditions. The system can process multiple faces simultaneously across a scene.

Utilises state-of-the-art deep learning AI — the same technology powering Google, Microsoft, Amazon, and Facebook's facial recognition systems.

Security Scenarios

Identification of blacklisted persons, suspects, and wanted individuals
Staff, employee, and contractor identity verification
VIP and personnel recognition for access and service personalisation
Detection of intruders and unauthorised persons in secured zones
Image-based search — find any person in recorded footage by face
Automated attendance — ins and outs without cards or PINs
General surveillance and crowd monitoring
Identifying missing persons and children

Platform Features

Real-time face matching against centralised database
Instant push notifications with match snapshot and confidence score
Live dashboard with identification event log
Works with single image enrollment per person
Centralised face database — reusable across multiple locations
Compatible with IP, PTZ, and vehicle-mounted cameras
Works on previously recorded video and image databases
3-month video storage with face-indexed event search
Optional GPU/AI accelerator support for faster processing

Key Advantages

Completely Contactless

No cards, fingerprints, or PIN pads. Subjects do not need to cooperate — identification happens automatically and discreetly.

Crowd-Scale Identification

Identifies multiple individuals simultaneously in a single frame — effective in railway stations, stadiums, and public gatherings.

Vehicle-Mounted Deployment

Can be deployed on a moving vehicle or kiosk — enabling mobile surveillance at events, borders, and checkpoints.

Centralised, Reusable Database

Face enrolled at one location can be matched across all deployed sites — no re-enrollment needed at each location.

Real Deployments

Live System Screenshots

These are actual captures from our Face Recognition system deployed at Mumbai Railway Station — operating in real crowds from a vehicle-mounted PTZ camera.

Face recognition at Mumbai Railway Station — crowd detection

Face Recognition — Mumbai Railway Station (Vehicle-Mounted PTZ Camera)

Face recognition identification in crowd

Multi-Face Identification in Dense Crowd Environment

AI face recognition crowd scene with detection overlays

Face Detection & Identification Overlays — Multi-Person Scene

Face recognition demonstrated in a moving crowd at a busy public transit location — processing multiple subjects simultaneously.

Applications

Where It's Deployed

Law Enforcement

Police departments use face recognition to identify suspects, missing persons, and blacklisted individuals in public spaces and CCTV footage.

Railway Stations & Airports

Monitor busy transit hubs for wanted persons, identify missing travellers, and manage VIP movement through high-footfall areas.

Critical Infrastructure

Government facilities, power plants, data centres, and defence installations where only authorised personnel may enter.

Corporate & Office Campuses

Touchless employee attendance, visitor management, and automatic security alerts when unknown persons are detected in restricted areas.

Religious & Public Events

Large gatherings at religious sites, stadiums, and political rallies where crowd-scale identification is needed without individual cooperation.

Schools & Institutions

Automate student attendance, prevent unauthorised visitors from entering school premises, and identify missing children rapidly.

Honest Assessment

Advantages & Limitations

We believe in transparency — here's exactly what our face recognition can and cannot do.

Advantages

Non-invasive, discreet, and time-saving — no subject cooperation needed
Identifies multiple individuals simultaneously in a single frame
More accurate and faster than human security personnel
Works with all camera types including PTZ and vehicle-mounted
Deployable on vehicles, kiosks, and fixed camera systems
Processes existing recorded video — retroactive identification possible
Single image enrollment per person supported
Centralised database reusable across all deployed locations

Known Limitations

Performance degrades in very poor lighting and extreme head angles
Requires a pre-built database — cannot identify persons not enrolled
Cannot reliably discriminate between very similar faces (e.g. identical twins) unless multiple reference images are provided
Accuracy may reduce with significant changes in age or appearance
For high-stakes use cases (suspects, VIPs), should be paired with secondary biometric verification
Higher computational demands — benefits from GPU/AI accelerator hardware

Minimum Hardware Requirements

IP camera with accessible video stream
Server: Intel i7 (8 cores), 8GB RAM, 500GB SSD
Internet or LAN connection
Optional: GPU or AI accelerator for faster throughput

Software Stack

Linux (Ubuntu / Debian-based OS)
Deep learning face recognition engine
Centralised face database with UUID management
Dashboard with event log and alert system

Face Recognition Service