Rank One Computing, a Gold Sponsor at Identity Week America 2026, has announced its technical achievement as the top tier performer across the full set of accuracy benchmarks in the NIST Friction Ridge Image and Features Technology Evaluation Exemplar One-to-Many (FRIF TE E1N). This recognition underscores ROC’s accuracy as a biometric vendor for Class B1 (4-4-2 Slap Fingerprints), positioning the company as the trusted domestic ABIS alternative to legacy foreign biometric technology. In terms of procurement decision measures, Class B is a heavily weighted metric for national ID systems and border control systems across global markets. These results validate the strength and accuracy of ROC’s technology and its market differentiation as the sole American-made provider.
“For years, the United States has maintained a dangerous overreliance on foreign AI for our most critical identity and biometric screening systems. Nearly every large-scale government deployment depends on overseas technology. ROC’s historic performance in the NIST fingerprint evaluation proves America now has a world-class domestic alternative — one that is more accurate, more efficient, and built here at home,” said B. Scott Swann, CEO of ROC.
“For decades, billions of dollars supporting critical U.S. identity infrastructure flowed overseas instead of strengthening America’s own industrial base. That changes today. National security systems should be powered by technology that is accurate, scalable, cost-effective, and aligned with American interests. We look forward to partnering with agencies across the FBI, DHS, and DoD to strengthen America’s technological independence and restore U.S. leadership in biometric and identity technologies,” concluded Mr. Swann.
ROC’s technical superiority is illustrated by returning the lowest error rate across a customary cohort of Class B identification error rate metrics. ROC also obtained top accuracy for False Negative Identification (FNIR) at Rank-12 for the same Class B identification error rate metrics. With compounding technical validation, ROC is establishing critical identity infrastructure built on its trusted domestic technology.
In practical use, ROC’s highly efficient, interoperable Vision AI platform is designed to simplify deployment and operations for high-stakes mission operators across law enforcement, homeland security, and civil ABIS environments. With advanced fingerprint recognition and broader multimodal biometric capabilities in one American-made platform, ROC believes it is poised to be the U.S. government’s choice application at scale to eliminate its reliance on foreign technology, reduce infrastructure complexity, and lower deployment costs.
“In just four months, ROC went from a strong first submission in FRIF E1N to the best in the world across substantial portions of the evaluation. This directly contrasts competitors who have been submitting against these same datasets for over a decade. The collaboration across our data engineering, research, and engineering teams during this sprint has been hard to overstate. This is only our second submission, and as a highly focused American biometrics company, ROC still has a lot of gas left in the tank for what comes next,” added Josh Engelsma, Principal Scientist at ROC.
NIST benchmarks are widely referenced in federal, defense, and international agency vendor evaluations. With its unified Vision AI platform, ROC addresses the escalating market demand for forward-thinking identification and intelligence solutions, strengthening both mission outcomes and business fundamentals. For customers, it delivers NIST-ranked biometric accuracy with reduced infrastructure complexity and total cost of ownership. For ROC, it supports a scalable software model with a durable recurring revenue profile and attractive margins.
The NIST FRIF E1N is widely regarded as one of the most comprehensive benchmarks in the biometrics industry. It establishes the standard for large-scale Automated Biometric Identification Systems (ABIS) used by governments and enterprises worldwide.
