Introduction
Face recognition is a technology that helps identify and recognize the face of a person whose image is securely stored in a database. Identification techniques like fingerprint, iris, and retina, may be perfect. However, facial recognition has been the main focus for many researchers since it is convenient and non-obstructive, as well as, easy to implement.
A face recognition algorithm is among the key components of a facial detection and recognition solution. The algorithm typically performs several main tasks, which include the following:
- Recognize faces in live streams, videos, and images
- Calculate a carefully worked-out model of the race image
- Match the mathematical model derived from the face image to a photo in a database or training set
So how can you make a face recognition Algorithm? And how do you ensure it’s working perfectly? After successfully creating your face recognition algorithm, you need to submit it to the National Institute of Standards and Technology to assess its performance; that is where FRVT 1:1 Verification and FRVT 1:N Identification come in.
This article will help you know how you can make a face recognition algorithm and how NIST can assess its performance. Let’s learn more.
How to Make a Face Recognition Algorithm
To create a face recognition Algorithm, you need to go through several simple steps, which include the following:
- Define your project scope
- Agree on your project methodology
- Formulate a development approach
- Estimate and plan your project
- Establish a complete project team
- Sign-up for a well-managed cloud service
- Get a tool for a face recognition algorithm development
- Sign up for a bulk SMS solution
- Find a test automation aid to boost your test coverage
- Design the user interface
- Develop the application for Android, iOS, or any other popular operating system
After going through these steps, submit your algorithm to the National Institute of Standards and Technology for the Face Recognition Vendor Test (FRVT), which includes FRVT 1:1 Verification and FRVT 1:N Identification.
FRVT 1:1 Verification
NIST’s FRVT 1:1 Verification aims to assess an algorithm’s ability to determine if two different images represent the same individual. This assessment involves using two photos; a gallery and a probe image.
The gallery includes photos already registered in the system, while the probe image is used for facial identification and recognition attempts. A gallery allows for comparison with a probe image.
FRVT 1:N Identification
NIST’s 1:N Identification helps assess an algorithm’s performance in terms of detecting specific faces from a huge gallery of photos. This evaluation features specific subtests based on the database used for gallery and probe images. In NIST’s FRVT 1:N Identification, the NNIR-FPIR metric is utilized.
FNIR represents the rate at which faces are incorrectly recognized as non-registered. On the other hand, the FPIR represents the rate at which non-registered faces are incorrectly recognized as enrolled.
