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.

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.

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.

Evergreen Licensing

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 […]