PFT II and PFT III (Proprietary Fingerprint Template) Evaluations

Different versions of Neurotechnology's fingerprint recognition algorithm were submitted to the NIST Proprietary Fingerprint Template Evaluation. The algorithm's template matching accuracy was among the best participants at the previous PFT II evaluation. Our latest submissions to the PFT II and the ongoing PFT III are in average the most accurate algorithms in all the experiments. See our comments for more information.

MINEX III Compliance and previous MINEX evaluations

In 2019 Neurotechnology's fingerprint template generator algorithm has been ranked the first in the NIST MINEX interoperability category; the fingerprint matching algorithm has also been ranked as the front-runner in terms of interoperability and, when combined, the two have become the supreme accuracy, high speed fingerprint recognition system. These results on continuously expanding participating vendors' algorithm set have confirmed and retained the Neurotechnology's MegaMatcher SDK position as the leading fingerprint recognition software in terms of both performance and reliability demonstrated since Neurotechnology's significant breakthrough submission in 2018. Also, in 2018 the latest fingerprint algorithm for smart cards submission has shown significant improvement in reliability since 2016 with proven outstanding template generator at enhanced performance, demonstrating significantly lower error rates than minimal interoperability and minimal accuracy specifications.

In 2017 MegaMatcher SDK fingerprint technology was ranked as the first most interoperable matcher and the fourth most accurate native template matcher vendor among all MINEX III compliant matchers. In 2016 MegaMatcher on Card SDK fingerprint matching algorithm for smart cards also successfully passed MINEX III evaluation.

Our comments on MINEX III participation contain more details about the results.

In 2014 MegaMatcher SDK fingerprint technology was recognized by the NIST as fully MINEX compliant and placed second in the Ongoing MINEX ranking for fingerprint matching algorithms.

In 2007 previous version of MegaMatcher SDK was one of the several algorithms worldwide recognized as fully MINEX compliant for both fingerprint template encoding and matching.

SlapSeg III Evaluation

Neurotechnology's slap fingerprint segmentation algorithm has been judged by NIST as the most accurate among the SlapSeg III 2 inch and 5.5 inch category participants, as well as second most accurate in the 8 inch category. See our comments for more information.

IREX 10 Results and Previous IREX Evaluations

In 2020 Neurotechnology's iris recognition algorithm has been judged by NIST as the second most accurate among the IREX 10 participants. The submitted algorithm featured much faster template creation and search time, and much smaller template size than the only more accurate contender. Our comments on IREX 10 participation contain more details about the results.

Previously, Neurotechnology showed perfect results in the IREX, IREX III, IREX IV and IREX IX evaluations.

FVC-onGoing results

In 2020 Neurotechnology's fingerprint recognition algorithm has shown the top result at the FVC-onGoing evaluation. The fingerprint extractor and matcher, which are included in the MegaMatcher SDK, were ranked as the most accurate for both FV-STD-1.0 and FV-HARD-1.0 benchmarks.
Read the press release for more information.

In 2019 MegaMatcher SDK palm print matching algorithm has shown the top result at the FVC-onGoing evaluation. The Palm Print Matcher was the most accurate overall and fastest among the five most accurate matchers.
Read the press release for more information.

FRVT 1:1 and 1:N Ongoing

In 2022 Neurotechnology facial recognition algorithm scored among the top algorithms in both 1:1 verification and 1:N identification scenarios. The algorithm ranked in the top 3% most accurate algorithms for 1:1 verification border control supervised (Visa Border, Border) and unsupervised (Kiosk) scenarios, as well as for recognition accuracy with face masks. Also, the algorithm ranked in the top 4% of the leading results matching frontal and profile mugshots scenarios for 1:N identification, as well as top results among border control supervised (Visa vs Border, Border vs Border ΔT ≥ 10 YRS) and unsupervised (Visa vs Kiosk) scenarios.

Our comments on FRVT 1:1 and FRVT 1:N participation contain more details about the results.

Kaggle Competition on DNN-based species classification

In 2017 Neurotechnology researchers won first place in a Kaggle competition with deep neural network based computer vision solution for classifying fish species.

Read the press release for more information.

FpVTE 2012 and FpVTE 2003 (the Fingerprint Vendor Technology Evaluations)

In 2015 Neurotechnology's fingerprint identification algorithms have been judged by the National Institute of Standards and Technology (NIST) as one of the fastest and most accurate among the participants. Our comments on FpVTE 2012 participation contain details about the results in each category.

Previously, Neurotechnology participated in FpVTE 2003 under the name Neurotechnologija and showed one of the best reliability results in the Middle Scale Test. See the FpVTE 2003 web site for a detailed report of the evaluation results.

FIVE (Face in Video Evaluation)

In 2015 Neurotechnology face recognition engine was submitted to the NIST Face in Video Evaluation (FIVE). In average the submitted algorithm was ranked among top 8 most accurate face recognition algorithms out of 16 vendors. See our comment for more information.

WSQ 3.1 Certification

In 2011 FBI certified Neurotechnology's implementation of WSQ image format support. Certificates and additional information are available.

FVC2006, FVC2004, FVC2002 and FVC2000 results

Neurotechnology participated in the Fingerprint Verification Competition several times and won numerous medals for reliability and performance. See the FVC2006 participation results, as well as FVC2004, FVC2002 and FVC2000 results for more information.

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.