April 16, 2012
VeriEye from Neurotechnology Judged
One of the Fastest and Most Accurate Iris Recognition Algorithms
in NIST IREX III Evaluation.
Neurotechnology's VeriEye Placed in the Top Four
for Iris Recognition Accuracy and the Top Two for Speed Among
Algorithms from 11 Participating Companies and Universities.
Vilnius, Lithuania – April 16,
2012 – The National Institute of Standards and Technology (NIST) has
judged the VeriEye iris recognition algorithm from Neurotechnology
to be among the fastest and most accurate in a field of more than 86
algorithms and variations from nine companies and two universities
in the NIST IREX III evaluation. The NIST Iris Exchange (IREX)
evaluation judges the top biometric algorithms for iris recognition
from providers around the world. This year's competition enrolled
more than 4 million irises captured with a number of different iris
scanners. Neurotechnology placed in the top four providers for
accuracy with its VeriEye algorithm, which was the fastest algorithm
at its accuracy level and also the second fastest algorithm overall.
This is the second time VeriEye has been among the top performers in
this prestigious evaluation of iris biometric technologies.
"The IREX III evaluation confirmed
that our core iris recognition technology, together with new
MegaMatcher Accelerator and MegaMatcher On Card algorithms,
continue to be among the most accurate algorithms," said Dr.
Justas Kranauskas, VeriEye Project Lead for Neurotechnology. "This
is particularly significant because they maintain this level of
accuracy even at the high speed required for large scale
The Iris Exchange (IREX) was initiated at NIST in support of an
expanded marketplace of iris-based applications based on
standardized interoperable iris imagery. The Iris Exchange IREX III
evaluation was conducted to measure the accuracy and speed of iris
identification algorithms in support of the development of
large-scale identification applications. According to NIST, "by
using as many as 6.1 million images of 4.3 million eyes, the results
are relevant to systems used for the full range one-to-many
applications including de-duplication, benefits fraud, and
token-less access and border control."
Neurotechnology's algorithms not only achieved some of the highest
levels of accuracy, they were able to maintain that high level of
accuracy while matching irises at a speed of more than 8 million
irises per second on a single CPU core. Neurotechnology delivered
the second fastest iris matching algorithm overall with the only
faster submission generating a three times higher False Negative
Identification Rate (FNIR) or "miss rate," when set at the same
False Positive Identification Rate (FPIR) or "false alarm rate"
According to NIST, the IREX III test ran algorithms on commodity
PC-class blade computers running the LINUX operating system, which
NIST identified as typical in central-server applications. The test
was designed to mimic real-world identification tasks and the
algorithms were invoked to do one-to-many searches in a database of
enrolled iris images to produce lists of candidate identities sorted
in increasing order of dissimilarity value. Two kinds of searches
were executed. The first, searches with an enrolled mate, allowed
measurement and reporting of the core FNIR. The second, searches for
which there is no enrolled mate, supported measurement of FPIR.
These quantities were estimated as a function of enrolled population
The NIST IREX III full report is available at:
www.nist.gov/itl/iad/ig/irexiii.cfm. Neurotechnology submitted
algorithms that included VeriEye at low, medium and high speeds,
MegaMatcher On Card and MegaMatcher Accelerator iris matching
algorithms. Neurotechnology's algorithms can be viewed as follows:
- N02A, N03A, N02B – correspond to the VeriEye algorithm
matching at low, medium, and high speeds.
- N04A, N11A – correspond to the MegaMatcher Accelerator iris
matching algorithm with a low speed of 5.6M irises per second on
- N12A – corresponds to the MegaMatcher Accelerator iris
matching algorithm (with default rotation tolerance of plus or
minus 15 degrees) with a high speed of 8.3M irises per second on
- N11B – corresponds to the MegaMatcher On Card iris matching
All submissions except N12A have a rotation tolerance increased to
plus or minus 25 degrees to improve performance on iris images
captured with single-eye cameras.
The VeriEye 2.5 iris recognition algorithm supports ANSI INCITS
379-2004 (American National Standard for Information Technology -
Iris Image Interchange Format) and ISO/IEC 19794-6 (Information
technology - Biometric data interchange formats - Iris image data)
The Software Development Kit (SDK) for VeriEye 2.5, as well as other
award-winning biometric technologies from Neurotechnology (including
MegaMatcher, VeriFinger, VeriLook and VeriSpeak) are available with
highly competitive licensing options through Neurotechnology or from
distributors worldwide. 30-day trial versions with full
functionality are also available for download.
Neurotechnology is a provider of high-precision biometric
fingerprint, face, iris, palmprint and voice identification
algorithms, object recognition technology and software development
products. More than 2500 system integrators, security companies
and hardware providers integrate Neurotechnology's algorithms into
their products, with millions of customer installations worldwide.
Neurotechnology's identification algorithms have consistently
earned the highest honors in some of the industry's most rigorous
competitions, including the National Institute of Standards and
Technology (NIST)'s Fingerprint Vendor Technology Evaluation
(FpVTE), the Iris Exchange (IREX) and the Fingerprint Verification
Drawing from years of academic research in the fields of
neuroinformatics, image processing and pattern recognition,
Neurotechnology was founded in 1990 in Vilnius, Lithuania and
released its first fingerprint identification system in 1991.
Since that time the company has released more than 80 products and
version upgrades for identification and verification of objects
and personal identity.