● Case Studies Facial Recognition System

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99.7% accurate biometric identification deployed across airports, law enforcement, corporate access control, and financial KYC.

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99.7%
Match accuracy on frontal face
0.3s
Recognition speed per face
96.4%
Accuracy with mask/partial occlusion
0.001%
False positive rate in field deployments
Case Studies

Biometric intelligence that delivers

CASE · 03 · 001

International Airport: 4 Million Passengers Processed Per Year at 9-Second Average

Major International Airport
India
48 processing lanes

A major international airport processing 4 million passengers per year needed to upgrade immigration lanes to handle growing passenger volumes while simultaneously strengthening security screening against an expanding watchlist of persons of interest. Average processing time of 44 seconds per passenger was causing significant queue build-up during peak hours.

48-lane simultaneous face recognition — CISF and immigration DB integration
Watchlist of 8,400 persons of interest — real-time alert on match
Mask and partial face recognition (96.4% accuracy maintained)
Document-to-face verification for passport/visa cross-check
Age and gender analytics for demographic security profiling
0.3 second processing with real-time dashboard for officers
44→9s
Average processing time per passenger
11
Watchlisted individuals detained in 6 months
0
False stops in first 6 months of operation

Processing time fell from 44 to 9 seconds per passenger — a 79% improvement that eliminated peak-hour queues. 11 watchlisted individuals were detained at departure without a single wrongful stop, maintaining passenger trust while significantly upgrading national security posture.

The accuracy is remarkable — even through masks, poor lighting, and passengers moving at pace. We haven't had a single false stop, which is critical in a public-facing environment.
— Senior Security Official, International Airport (name withheld)
CASE · 03 · 002

Law Enforcement: 200-Person Watchlist Deployed Across Metro City CCTV Network

Metropolitan Police
3,400 cameras integrated
Wanted persons / bail absconders

A metropolitan police force had 200+ bail absconders, parolees, and wanted persons actively evading arrest across a city of 8 million people. Manual surveillance and tip-off-based tracking was yielding fewer than 3 apprehensions per month on this list. The force needed a scalable way to automatically alert field units when listed persons appeared anywhere in the city's 3,400-camera network.

3,400 camera feeds monitored simultaneously against live watchlist
Alert includes clip, confidence score, GPS location, nearest patrol unit
Low-light accuracy maintained (91.2%) for night feeds
Profile-view matching for cameras at angles (93.8% accuracy)
False positive filtering with dual-match confirmation before alerting
Integration with patrol dispatch — SMS alert with face match image
3→31
Monthly apprehensions from watchlist
8.4min
Average time from alert to field response
97%
Alert confirmation rate (post dual-check filter)
CASE · 03 · 003

Banking KYC: Video Verification Reduces Onboarding Fraud by 88%

Private Sector Bank
2.4M remote KYC verifications/year
RBI V-CIP compliant

A major private bank processing 200,000 remote KYC verifications per month was seeing a 3.4% fraud rate on video KYC calls — customers presenting digitally altered documents, spoofed video streams, or impersonating others using photos. Manual reviewer catch rates were approximately 60%, leaving significant fraud exposure.

Real-time liveness detection — prevents photo/video spoof attacks
Document-to-face match (Aadhaar, PAN, passport photo vs live face)
Deepfake video detection using micro-expression analysis
Age estimation cross-check against declared DOB
RBI V-CIP (Video Customer Identification Process) compliance
Flagged sessions escalated to human reviewer with AI confidence score
88%
Reduction in KYC fraud rate
3.4→0.4%
Monthly fraud rate improvement
40%
Reduction in human reviewer workload
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