Introduction
Facial recognition technology is becoming increasingly popular. It continues to evolve at lightning speed, largely driven by massive advancements in the Artificial Intelligence industry. The latest facial recognition systems can determine gender, ethnicity, age and authenticity, despite physical and lighting obstructions like face masks, helmets, and glasses, among others.
Cutting-edge facial recognition systems are currently more attainable than before, making them affordable for small, medium-sized and large businesses in all sectors. So does your business need a facial recognition algorithm? If yes, which algorithm is right for you? Picking the best facial recognition algorithm requires careful consideration of different factors.
The main factor to consider when picking the best face recognition algorithm is the National Institute of Standards and Technology ranking. NIST conducts the Face Recognition Vendor Test (FRVT) to determine the best-performing face recognition algorithm. So how does NIST FRVT determine the best face recognition algorithm? Keep reading to discover more.
What is NIST FRVT?
NIST FRVT is the short form for the National Institute of Standards and Technology (NIST) – Face Recognition Vendor Test (FRVT). NIST develops guidelines and standards for the biometrics industry. The FRVT includes a set of evaluations that NIST conducts to assess face recognition algorithms’ performance. NIST assesses all face recognition algorithms that vendors from across the world submit.
The main goal of these evaluations is to provide the research community, business owners, government agencies, and different industries with unbiased, reliable, accurate and up-to-date information about face recognition algorithms’ performance. This allows organisations, company owners, agencies and individuals to make informed decisions when choosing the best system.
How Does NIST Determine the Best Face Recognition Algorithm?
NIST divides FRVT tests into two major categories: special and ongoing. The institution conducts special tests on a vendor-needed basis to assess specific scenarios or use cases. On the other hand, NIST conducts ongoing tests regularly to evaluate face recognition algorithms’ performance.
NIST performs these tests on closed Black Box datasets, meaning vendors can access any test image. This ensures that submitted systems aren’t just trained specifically for FRVT tests.
How Do You Read NIST FRVT Results to Pick the Best Face Recognition Algorithm?
The National Institute of Standards and Technology publishes all FRVT results in various formats, including report cards, reports and leaderboards for vendors who submit their face recognition algorithms. NIST FRVT leaderboards list comes in the form of a searchable, ordered-based, interactive table that vendors can use.
The report files have detailed tables with vendors who submitted their face recognition algorithms showing the performance of each based on error rates with standardised datasets. Lower error rates show that a face recognition algorithm is more accurate. Also, NIST provides every vendor with a scorecard that gathers its performance information in one report that you can access.
In conclusion, there is no one definitive answer to the question of which face recognition algorithm is best. Different algorithms may be more suitable for different use cases depending on factors such as accuracy, speed, and robustness to variations in lighting conditions, facial expressions, and occlusions. Some of the most commonly used algorithms include Eigenfaces, Fisherfaces, Local Binary Patterns (LBP), and Deep Convolutional Neural Networks (DCNN). Ultimately, the choice of algorithm will depend on the specific requirements of the application and the available resources. It is important to keep in mind that while face recognition technology can offer many benefits, it also raises important ethical and privacy concerns that must be carefully considered and addressed.
