The US General Services Administration (GSA) has announced an expansion for its Login.gov platform that could employ biometric scanning for the first time and boost user security
In this expansion, new identity verification options will be introduced to provide users with seamless access to government benefits while also keeping them and their data safe and secure.
By providing facial recognition and digital identity verification, users will now be able to verify their identity and access their services using their face.
Reducing fraud with your face
This latest login pathway is due to be released next year and will provide a high level of confidence in identity verification, aligning with the National Institute of Standards and Technology’s (NIST) 800-63-3 IAL2 guidelines.
You can also expect a high level of security for your data. As a government service any data used by Login.gov to verify your identity, or any other service, can not be sold or used for any other unrelated purposes.
Facial recognition is already used by Login.gov through the low-tech method of in-person verification at a participating US Postal Service branch with a Postal Service employee. With over 99% of the US population living within 10 miles of a Post Office, this method has helped strengthen fraud prevention.
According to the GSA blog, the facial recognition technology used by Login.gov will:
- Always protect user data by ensuring it will never be used for any purpose unrelated to verifying your identity by Login.gov or any vendors we contract with.
- Leverage best-in-class facial matching algorithms that, based on testing in controlled environments, have been shown to offer high levels of accuracy and reduced algorithmic bias.
- Use a privacy-preserving matching approach that compares “selfies” exclusively with the user’s photo ID—and does not use the image for any other purpose.
- Continue to invest in academic quality research, such as GSA’s equity study on remote identity proofing, to measure all aspects of Login.gov’s performance, including algorithmic bias across demographic factors.
