Rank One Computing Blog
A guide to automated face recognition algorithms.
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
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.
Hardware requirements for video processing applications – Part 1: Template generation
When automated face recognition technology is used for analyzing streaming video, an important question is: how much computer hardware is needed? The hardware required to process video depends on several factors which will be discussed in this article.
Hardware requirements for video processing applications – Part 2: Template comparison
In this article we explain how to factor in the computational demand for template comparison in video processing applications. While this task is not as computational burdensome as template generation, for larger-scale applications it can become meaningful.
Overview of ROC SDK Version 1.19
The ROC SDK version 1.19 delivers top-tier accuracy and industry leading efficiency. This new version comes with accuracy improvements, clustering enhancements, homomorphic encrypted matching, GPU enrollment, and several other enhancements.
How Forensic Face Recognition Works
Law enforcement primarily uses face recognition as a post-incident forensic tool to enable detectives and analysts to generate investigative leads in violent and harmful crimes. In this article we explain how forensic face recognition works, and how it is used by law enforcement in this country.
When Misinformation Endangers Lives
The use of automated face recognition in law enforcement is one of the most powerful tools available in today’s law enforcement investigations, and delivers substantial benefits to society without any documented cases of law enforcement misuse.
10 Steps for Selecting a Face Recognition Algorithm
Follow these 10 steps to success when selecting face recognition SDK or system.
Procuring a Face Recognition Algorithm: Efficiency Considerations
This article will equip you with the knowledge to assess the efficiency requirements of your face recognition system. In turn, you will be able to factor this important consideration into your procurement process and potentially eliminate certain algorithms before the time consuming step of performing internal evaluations.
Rank One continues strong performance in NIST FRVT Ongoing testing
Rank One delivered another impressive performance in the latest iteration of the NIST FRVT Ongoing face recognition benchmark. While nearly every vendor had gaps in their algorithmic performance, Rank One’s v1.18 algorithm did not have a single performance deficiency.
