Youverse highlights population-scale facial recognition accuracy in NIST evaluation

Youverse says its first submission to the NIST’s ongoing Face Recognition Technology Evaluation 1:N Identification track validates the accuracy of its software as suitable for population-scale fraud detection.

The company’s youverse_001 facial recognition algorithm recorded a false-negative identification rate of 0.43 percent at a threshold limiting the False Positive Identification Rate (FPIR) to approximately 0.3 percent when matching frontal mugshot probes against a gallery of 1.6 million mugshots. Youverse expresses this as a 99.57 percent true identification rate.

“Independent evaluation by NIST is a major milestone for Youverse and a powerful validation of what we have built,” says Miguel Lourenço, the company’s CPO.

“Achieving a 99.57 percent true positive identification rate against a gallery of 1.6 million faces demonstrates that our technology delivers the scale and accuracy demanded by the world’s most critical identity systems,” Lourenço adds.

One-to-many identification compares one probe image against an entire gallery to find a possible matching identity. Organizations often use 1:N identification to search large databases for duplicate enrollments or people registered under different identities.

When searching a NIST mugshot gallery with 12 million identities, the algorithm recorded a 98.97 percent identification rate.

Youverse says this large-scale search capability could support identity deduplication, fraudulent enrollment detection, access control, KYC, and anti-money laundering (AML) processes.

The company’s best result by ranking among the main FRTE metrics was matching probe images captured by biometric kiosks against a gallery of 1.6 million visa images.

Youverse delivers the facial matching algorithm with liveness detection and says its platform permits one-to-many searches only with explicit user consent.

The latest NIST FRTE 1:N results showed that performance continues to converge in controlled comparisons such as frontal mugshot identification. Important performance differences remain when algorithms face more difficult images, including webcam captures, profile views, and airport kiosk photographs.

Article Topics

accuracy  |  biometrics  |  Face Recognition Technology Evaluation (FRTE)  |  facial recognition  |  NIST  |  Youverse