Intelligent Computer Vision

Top-ranked Face, Object, & Text Recognition solutions...Lightning-fast, AI/ML-powered, Made in the USA
ROC's AI/ML SDK 2.XStay aware with live alerting from ROC Watch

The ROC Difference

Faster and more accurate

  • More than 99.4% accurate (NIST FRVT)
  • Ranked #1 in algorithm speed and efficiency
  • Detect persons, vehicles, text, and weapons in the same video feed
  • Process videos in real-time on mobile devices

Real-world Performance

  • Host millions of identities on a mobile device
  • Identify faces and objects without ideal camera angles or lighting
  • Proven FR accuracy with masks and sun glasses
  • Hundreds of successful integrations

Ethical & Trustworthy

  • American Nascency: 100% Made-in-America
  • Highest ethical and privacy standards (Code of Ethics)
  • Trusted by DoD, FBI, and Fortune 500 companies

Easy Integration

  • Free access to development SDK
  • OS and platform agnostic
  • Extensive documentation and sample code
  • Deploy in the cloud, on-prem, or on a mobile device
  • Works with existing camera and VMS systems
  • Be up and running in minutes
Don’t Just Take It From Us
Rank One’s algorithms are deployed in mission-critical applications across both the public and private sectors.
US Dept of Defense Agencies


Multi-National Financial Institutions

Law Enforcement Agencies


Integrator Customers


Facial Verifications Per Year

Stay Tuned

Learn more about AI/ML-powered computer vision from experts in the field.

Facial Recognition Code of Ethics

Facial Recognition Code of Ethics

Rank One Computing believes in a just, non-violent world of equality and fairness. We prize democratic values, civil liberties and open and informed debate. When used to further these values, automated face recognition can continue to make the world a safer, better place for everyone. In the absence of regulatory guidance, we wish to advance limitations that we believe are appropriate in how face recognition should be utilized.

The following set of ethics serve as a guideline for how we will develop face recognition systems and how we will expect our integration partners and end-users to develop and utilize face recognition systems based on our algorithms

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Race and Face Recognition Accuracy: Common Misconceptions

Race and Face Recognition Accuracy: Common Misconceptions

There is a misperception that face recognition algorithms do not work on persons of color, or are otherwise inaccurate in general. This is not true.  The truth is that across a wide range of applications, modern face recognition algorithms achieve remarkably high accuracy on all races, and accuracy continues to improve at an exponential rate.

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