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 […]
The ROC SDK version 1.18 is now available to Rank One’s user community. This release is one of the most exciting progressions to Rank One’s face recognition algorithms ever delivered, with substantial accuracy improvements and several new features.
Face recognition technology is rapidly expanding as a convenience technology that allows quick and secure access to sensitive systems and facilities, and for people to ubiquitously interact with their environments. This article highlights the most prominent commercial applications of face recognition technology that are emerging.
Choosing a face recognition algorithm that meets your accuracy requirements can be a daunting process. We simplify this process with a straightforward guide on how to measure algorithm accuracy and determine which algorithms are viable for your application.
Automated face recognition algorithms rely on highly complex mathematical models, but at a high level many of the techniques performed are rather easy to understand. In this article we provide a high level guide of how automated face recognition algorithms work.