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.
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.
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.
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.
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.
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.
Follow these 10 steps to success when selecting face recognition SDK or system.
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 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.
Perhaps no technology is improving as rapidly as automated face recognition. For example, over the last two years Rank One has reduced the False Non-Match Rate of our algorithm by over 10x: Other face recognition vendors are similarly improving their accuracy at a rapid pace. However, despite these relentless improvements, many vendors are also denying […]