CBP Plans to Expand Facial Recognition at U.S. Borders

U.S. Customs and Border Protection, referred to as CBP, is aiming to enhance its application of facial recognition technology at land border entries. In a recently released Request for Information (RFI), CBP is inquiring about solutions to capture facial images of people inside vehicles for verification purposes. This would support its Traveler Verification Service (TVS), which matches real-time images with stored biometric data to verify identities.

Facial recognition has become more important in protecting the country and its boundaries, as it secures the border by adding this extra layer of protection. The Department of Homeland Security agencies, including CBP, use technology mainly to process check-ins faster, prevent entry or exit of unauthorized individuals, and ensure effective enforcement of travel bans. Currently, the technology has already been deployed at airports and seaports and deployment at border crossings without causing traffic issues.

This reflects a national move to bring AI-based biometrics into identity control for the safe and swift inspection of structures vital to the nation. Even so, increasing endorsement means that ethics and transparency in adoption are necessary. For these systems to grow effectively, everything needs to remain clear, individual data must be kept safe, and the public needs to have trust in them.

CBP promises that it will comply with confidentiality and privacy laws, and it is still communicating with industry leaders on this topic.  FACIA supports CBP’s expansion by offering accurate, inclusive facial recognition that ensures regulatory compliance across borders and real-time identity verification. Its technology prevents unauthorised access and illegal crossing at high-traffic land borders.

As a leader in biometric fraud prevention, FACIA helps governments and businesses responsibly use face recognition by enabling compliance with data protection regulations. Its solution is built on the core principle of inclusion, ensuring accurate recognition of ethnically diverse users and supporting broader applicability across varied populations.