Why FaceFirst?
FaceFirst solves operational challenges unique to retailers, airports, event venues, and casinos, among others.
Proven
Successes for Retail and Specialty
Use Cases
Highly accurate results even with challenging camera angles and lighting conditions.
Excellent Performance in Real-World Environments
Proprietary algorithms developed in-house with neural networks and deep machine learning.
AI Enabled With All Algorithms Developed
In-House
FaceFirst sends match notifications with recommended response based on your approved policy.
Real-Time
Notifications
and Actionable Intelligence
Plug-and-play deployment with existing cameras and easy-to-use design for fast success
and ROI.
Low Implementation Costs, Fast Deployment and ROI
FaceFirst: Your Fast, Accurate, Ethical Face Matching Platform
FaceFirst is a global leader in highly effective face matching systems for retailers, hospitals, casinos, airports, stadiums, and arenas. FaceFirst’s software leverages artificial intelligence and human oversight to prevent violence, theft, and fraud. With FaceFirst, you can provide safer environments for your valued customers, patients, guests, employees, and associates. We design our patented video analytics platform to be scalable, fast, accurate, and ethical while maintaining high levels of security, privacy, and accountability. FaceFirst is based in Austin, Texas.
Computer Vision and Face Matching Transformed
It’s tedious to search video footage manually to identify persons of interest, but not with FaceFirst’s visitor search capabilities. Our powerful search tools can reduce your investigation time from weeks to minutes.
After you enroll a known offender in your custom database, the FaceFirst algorithm can find every recent instance of that person entering all your locations. The result? A human investigation with advanced AI confirmation that calculates past losses, plus date/time stamped evidence packaged for law enforcement agencies and prosecutors.
A retailer that uses the FaceFirst search feature after each enrollment uncovered an average of from five to 22 other thefts or incidents with the same offender before the enrollment.
Let us show you how to identify multiple prior visits, assess prior losses, and build prosecutable cases against habitual offenders with just one click.
Technology Partners
