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.
A comprehensive set of definitions and terms used when discussing face recognition technology.
Automated face recognition (FR) is one of the fastest growing and most exciting technologies in the world. This blog will keep readers informed on all facets of integrating and using automated face recognition algorithms.