Speed is measured on Intel Xeon CPU E5-2630 v4 @ 2.20GHz. Source: NIST FRVT Ongoing.
Perform 1:1 verification with industry leading accuracy.
Search millions of faces on a mobile device, or billions on a server, in seconds.
Locate faces in photos and video with industry leading detection speed.
Track eye and chin locations.
Group unlabeled faces by identity.
Predict a face likelihood to generate a successful match.
Analyze a face to predict its age, ethnicity and gender.
Analyze a face for anger, disgust, fear, joy, neutral, sadness or surprise expressions.
Process videos in real-time using a single core on a mobile device or server.
Determine where a person is looking relative to the camera using roll, yaw and pitch measurements.
Detect print and digital screen replay attacks using a single image.
Count persons in a video stream seamlessly.
Measure template similarity within the encrypted domain.
Detect eyeglasses, sunglasses, or no glasses.
Optionally enroll images on a GPU.
Determine if a face is of a cartoon, a painting or a human.
No internet connection is required.
Support for all major programming languages including Python, Java, C# and Go.
A robust command line interface allows constructing systems from shell scripts.
When building mobile or embedded face recognition applications, there is a small amount of computer memory available. Thus, only face recognition algorithms that require a limited amount of RAM can be used in mobile and embedded applications. This article discusses these concepts and highlights how many vendors develop algorithms that are not usable in mobile and embedded applications.
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.
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.
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. This article highlights the importance of an evergreen licensing approach to ensure you remain at the cutting edge of facial recognition capabilities.
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.