IREX IV Evaluation
In 2013 Neurotechnology's iris recognition algorithm has been judged by the National Institute of Standards and Technology (NIST) as one of the fastest and most accurate among the participants.
The fourth stage of the NIST Iris Exchange (IREX) evaluation included 66 identification algorithms submitted by 12 companies and research institutions. The Neurotechnology iris matching algorithm has shown that its accuracy is in the top 4. Also, the Neurotechnology algorithm was the second fastest and provided almost 6 times higher recognition accuracy that the only faster contender.
The IREX IV homepage contains downloadable full report of the NIST IREX IV. Neurotechnology submitted algorithms are denoted as B00P, B01P, B02P, B00N, B01N and B02N.
IREX III Evaluation
In 2012 VeriEye iris recognition algorithm has been judged by the National Institute of Standards and Technology (NIST) as one of the fastest and most accurate among the participants.
The third phase of the NIST Iris Exchange (IREX) evaluated more than 86 algorithms and variations from 9 companies and 2 universities. Neurotechnology iris matching algorithm was the second fastest and provided 3 times higher recognition accuracy than the only faster contender.
The IREX III homepage contains downloadable full report of the NIST IREX III, as well as a number of IREX-related documents. Neurotechnology submitted algorithms are denoted as:
See also our press release for more detailed overview.
WSQ 3.1 Certification
In 2011 FBI certified Neurotechnology's implementation of WSQ image format support.
The FBI Criminal Justice Information Services Division together with the National Institute of Standards and Technology (NIST) have reviewed and analyzed testing information submitted by Neurotechnology. Based on the testing results, the FBI has certified that this implementation meets the accuracy requirements in the Wavelet Scalar Quantization (WSQ) Gray-Scale Fingerprint Image Compression Specification, Version 3.1.
You may download the PDF file with the scanned certificates. The PDF file contains seven separate certificates, as each of them was issued for a particular computing platform.
See our press release for more information.
In 2009 VeriEye SDK was recognized by the National Institute of Standards and Technology (NIST) as one of the most reliably accurate iris recognition algorithms among those tested. The VeriEye engine is also included in the MegaMatcher SDK.
The NIST Iris Exchange (IREX) Evaluation judged 18 state-of-the-art algorithms from 10 different providers and found VeriEye to be in the top 3 for accuracy. Neurotechnology had the fastest overall iris matching algorithm – 25 to 75 times faster than other top ranked competitors – while utilizing an iris template that is 3.5 to 7.5 times smaller than those same competitors' templates. Neurotechnology was the only provider to achieve such high rankings in all these areas.
IREX testing focused on compressed image recognition and interoperable formats in an attempt to provide data standards to support data exchange, storage and "smart card" use.
In 2007 MegaMatcher SDK fingerprint technology was recognized by the National Institute of Standards and Technology (NIST) as fully MINEX compliant. This MINEX compliance recognition allows to use MegaMatcher in Personal Identity Verification program (PIV) applications. MegaMatcher fingerprint technology is also used in VeriFinger SDK.
The Minutiae Interoperability Exchange Test (MINEX) evaluates fingerprint template encoding and matching to determine compliance with the government's PIV program for the identification and authentication of Federal employees and contractors. The MINEX program provides measurements of fingerprint algorithm performance and interoperability to both government and commercial entities.
In 2007 MegaMatcher was one of the several algorithms worldwide recognized as fully MINEX compliant for both fingerprint template encoding and matching. This recognition puts MegaMatcher SDK into the U.S. government buyers' certified list of fingerprint recognition algorithms.
See also our press release.
Fingerprint Verification Competition (FVC2006)
Neurotechnology is pleased to announce that our results in the Fingerprint Verification Competition (FVC2006) achieved the highest ranking when using the most realistic benchmark for real-world biometric applications, "Average Zero FMR."
Neurotechnology participated in FVC2006 under the name Neurotechnologija. In 2008 the company changed its corporate name to Neurotechnology.
FVC2006 Open Category results.
The whole page is available at the FVC2006 web site.
Neurotechnology algorithm is denoted there as P058.
Neurotechnology also won four gold medals, two silver and two bronze medals in the FVC2006 Open Category.
Our algorithm took second place in the FVC2006 Light Category, according to the Average Zero FMR benchmark. The algorithm won one gold and four bronze medals in this category.
Considering Competition Results in Real-World Applications
For each participating algorithm, the Fingerprint Verification Competition (FVC2006) measured several reliability parameters, including:
When considering the results of competitions, it is important to put the competition criteria into the perspective of real-world biometric applications.
The goal of many real-world applications of biometric technology is to let the "good guys" in while keeping the "bad guys" out. In most security situations, keeping a few of the "good guys" out is more acceptable than letting a few of the "bad guys" in. Thus, most real-world applications of biometric technology are set to have a FAR as close to zero as possible. A 0.001 % FAR is common and sometimes 0.0001 % FAR or even less are used. This minimizes the number of people who are incorrectly accepted into the system (or allowed entry). When the FAR is low, the FRR is higher, which means the system may incorrectly refuse entry to someone who should be there. A more reliable algorithm means you will have a lower FRR when the FAR is very low (near to zero).
In this sense, other than EER, which represents reliability in the very high FAR area only, the Zero FMR rate is the most adequate benchmark for evaluating real-world biometric applications.
See also our press release.
The Fingerprint Vendor Technology Evaluation (FpVTE 2003)
Conducted by the National Institute of Standards & Technology (NIST) on behalf of the Justice Management Division (JMD) of the US Department of Justice.
Neurotechnology participated in FpVTE 2003 under the name Neurotechnologija. In 2008 the company changed its corporate name to Neurotechnology.
Neurotechnology's algorithm achieved one of the best reliability results in the Middle Scale Test among FpVTE 2003 participants:
* Results shown from the NIST FpVTE 2003 do not constitute an endorsement of any particular system by the government.
FVC2004, FVC2002 and FVC2000 results
Organized by Biometric Systems Lab (University of Bologna), Pattern Recognition and Image Processing Laboratory (Michigan State University) and the Biometric Test Center (San Jose State University)
Neurotechnology participated in FVC2004, FVC2002 and FVC2000 under the name Neurotechnologija. In 2008 the company changed its corporate name to Neurotechnology.
Neurotechnology's algorithms consistently showed some of the best reliability results among participants, earning the following awards:
Since the FpVTE 2003 and FVC2004 competitions were held, Neurotechnology has developed many algorithm improvements on the versions tested in the contests (both algorithms were submitted in 2003). New fingerprint filtration functions were developed, allowing better filtration of low quality images. Additionally, the generated templates size has been decreased from 300 - 600 bytes to 150 - 300 bytes per fingerprint by using features set optimization. Also, identification speed has been increased from 5% to 100%, depending on the number of fingerprint minutiae. All these improvements allow us to achieve even better results in our products.
Comments on competition results
The FpVTE protocol was strict and did not allow use of some of our advanced algorithm features, which, in a real world application, would further increase the recognition quality. Particularly, the MST set contained images from different scanners, but each particular image scanner model was not disclosed. In a real world scenario, specific parameters would be set for each specific scanner type. This would allow the algorithm to perform at an even higher accuracy level.
Another such real world example that was not simulated in the FpVTE protocol is the ability to generate globalized or generalized features templates by capturing several images from the same finger and combining the templates into a single features set. Using a generalized features set can significantly improve the algorithm's reliability and produces improved matching scores. In the FpVTE MST set such a method could not be used, as only two matched fingerprints were allowed for consideration.
The FVC protocol is very useful for comparing different vendors' algorithms, however it only allows comparison of verification (1-to-1 matching) but not identification (1-to-many matching) results. One of the strongest capabilities of Neurotechnology's algorithms is fast, reliable identification, therefore a 1-to-many test would better reflect our real algorithm ranking among the participants.
FVC uses databases that are not from real applications (more information), but rather uses fingerprint sets which had been specially collected for the competition (some with certain distortion or noises highlighted). In this way, various distortion and noise statistics of the fingerprints did not correspond to real world application statistics, and vendors' results may be not completely adequate to apply to real life situations.
Like the FpVTE, the FVC did not allow us to generate globalized or generalized features templates by capturing several images from the same finger and combining the templates into a single features set. Using a generalized feature set can significantly improve the algorithm's reliability and produces improved matching scores. In the FVC such a method could not be used, as information from only two matched fingerprints was allowed for consideration.
NIST Proprietary Fingerprint Template (PFT) Testing
Since June 2003, NIST has been conducting tests of fingerprint-based biometric matching systems using vendor supplied SDKs. The main result obtained from these evaluations is an estimate of how well commercial products performed one-to-one matching for verification over a wide range of fingerprint image qualities.
Unlike the Fingerprint Vendor Technology Evaluation (FpVTE), these evaluations are ongoing and new SDKs can be included in the test at any time.
The latest MegaMatcher SDK was tested in NIST PFT Testing and the results are available under the "1T" label.
Neurotechnology will be exhibiting at CARTES 2014 on November 4-6.
Meet us at booth 4 K 105.
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