Those in the biometrics industry know intuitively that facial recognition has advanced by leaps and bounds in recent years, but a new report from the National Institute of Standards and Technology (NIST) puts numbers to just how far the state of the art has come.
NIST, which is the world’s premier agency in the evaluation of biometric technologies against defined standards, has just published its latest report, “NIST Interagency Report (NISTIR) 8238, Ongoing Facial Recognition Vendor Test (FRVT)”; and the organization has compared the data therein to previous reports, finding significant jumps in accuracy and other metrics.
Assessing 127 software algorithms from 39 providers – “the bulk of the industry”, the organization said in a statement – NIST found that on average, between 2014 and 2018, facial recognition technology “got 20 times better at searching a database to find a matching photograph”. Other metrics further elaborate the progress. In 2010, five percent of algorithms failed to match a face in a given database. This year, only 0.2 percent failed in their searches.
That having been said, “[t]here remains a very wide spread of capability across the industry,” commented NIST computer scientist Patrick Grother. But Grother attributed much of the recent advancement to machine learning technology, and in particular to systems based on convolutional neural networks, a machine learning structure inspired by biological processes. “About 25 developers have algorithms that outperform the most accurate one we reported in 2014,” thanks to the emergence of these technology, Grother suggested.
NIST’s full report, and those from previous years, are available from the organization’s website.
December 6, 2018 – by Alex Perala