ROC SDK v1.20 delivers top-tier accuracy and unparalleled efficiency in latest NIST 1:N report
The latest NIST FRVT 1:N report demonstrates the unique combination of accuracy and efficiency delivered by the ROC SDK as compared to industry peers. To learn more read our latest blog post.
ROC SDK v1.20 delivers top-tier accuracy and unparalleled efficiency in latest NIST 1:N report
The latest NIST FRVT 1:N report demonstrates the unique combination of accuracy and efficiency delivered by the ROC SDK as compared to industry peers.
Rank One Computing outpaces industry in memory requirements
For the first time in the January 6th, 2020 report, the National Institute of Standards and Technology (NIST) Face Recognition Vendor Test (FRVT) “Ongoing” benchmark is reporting the peak memory usage of each face recognition algorithm. This measurement, which...
Understanding the Importance of Peak Memory Usage
When building mobile or embedded face recognition applications, there is a small amount of computer memory available. Thus, only face recognition algorithms that require a limited amount of RAM can be used in mobile and embedded applications. This article discusses these concepts and highlights how many vendors develop algorithms that are not usable in mobile and embedded applications.
Rank One participates in face recognition policy panel in Washington, D.C.
On Dec. 5, the Information Technology and Innovation Foundation (ITIF) hosted a briefing in Washington, D.C., exploring emerging uses of facial recognition in the private sector. The discussion – moderated by Daniel Castro, vice president of ITIF and director of...
Now Available: ROC SDK version 1.20, featuring substantial accuracy improvements and a more robust API
Rank One announces a major enhancement to the ROC face recognition SDK with the newly released version 1.20. The most significant improvement is in face recognition accuracy, with as much as a 40% reduction in error rates. The embedded algorithm algorithm that ships...
Overview of ROC SDK Version 1.20
The ROC SDK version 1.20 delivers major accuracy improvements, alongside a wide range of other algorithmic and functionality enhancement.
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
Rank One joins ITIF’s “Open Letter to Congress on Facial Recognition”
Rank One Computing joined prominent research organizations, law enforcement groups, and technology companies last week in sending an open letter to Congress that clarifies the misinformation being circulated regarding the technology, and pledges to support properly...
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.