Face Recognition for Train, Subway & Railway Centers
Face recognition technology for railways eclipses traditional video surveillance in effectiveness and efficiency.
We currently power the world’s largest and successful airport face detection program, and FaceFirst works just as well for railways.
Accelerate Customs at Borders
FaceFirst greatly expedites customs by recognizing faces far faster and more accurately than humans.
Instantly Actionable Intelligence
FaceFirst detects rail travelers who potentially pose a threat and then instantly provides your security team with actionable alerts that tell them who to look for, where to go and what to do.
Privacy by Design
FaceFirst is built from the ground up to ensure that privacy is a top priority.
A Face in a Crowd
FaceFirst easily identifies and logs individual faces within crowded train stations and transportation hubs.
FaceFirst notices what the human eye won’t. Our system easily sees past eyeglasses, wigs, hats and partial face coverings.
Intelligent Surveillance Analytics
FaceFirst provides a host of intelligent analytics that help railways prepare for future threats. Ensure that train or subway stations are always adequately staffed with the security personnel needed to deter threats.
Verify Identity in Seconds
FaceFirst instantly confirms whether a traveler is really who they say they are.
Smarter, Effective Searches
Never again spend hours looking through traditional surveillance footage for a facial match.
FaceFirst works with existing surveillance cameras and integrates to store systems via API.
Face Recognition for Railways and Public Transportation Centers
FaceFirst helps safeguard train, subway and other railway hubs by proactively deterring terrorism and crime through enterprise biometric surveillance, mobile solutions and predictive analytics. The FaceFirst patented facial recognition platform leverages real-time alerting across an unlimited number of train or subway stations, providing security officers with actionable information when they need it most.