Neurotechnology company logo
Menu button

Technology Awards

Fingerprint recognition algorithms

MINEX III Compliance and previous MINEX evaluations

In 2023 Neurotechnology's fingerprint template matching algorithm achieved first place in the NIST MINEX III evaluation. Combined with the existing first position in the template generator interoperability category, Neurotechnology is the top vendor within the MINEX III evaluation overall. These results on continuously expanding participating vendors' algorithm sets have confirmed and retained Neurotechnology's position as the leading fingerprint recognition software vendor in terms of both performance and reliability since Neurotechnology's first-place achievement in the template generator algorithm category in 2019. 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.

PFT III (Proprietary Fingerprint Template) and previous PFT Evaluations

In 2023 Neurotechnology's fingerprint recognition algorithm has shown the most accurate results in most of the experiments at the PFT III. See our comments for more information.

Previously, different versions of Neurotechnology's fingerprint recognition algorithm were submitted to the NIST Proprietary Fingerprint Template Evaluation. The algorithm submissions showed the best overall template matching accuracy at the previous PFT II evaluation.

SlapSeg III Evaluation

Neurotechnology's slap fingerprint segmentation algorithm showed off as a top performer in the SlapSeg III evaluation, featuring the fastest performance and almost the best accuracy in most categories of the SlapSeg III evaluation. See our comments for more information.

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.

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.

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.

Palmprint recognition algorithms

FVC-onGoing results

In 2019 Neurotechnology's palmprint matching algorithm has shown the top result at the FVC-onGoing evaluation. The palmprint matching engine of MegaMatcher SDK was recognized as the most accurate overall and fastest among the five most accurate matchers.
Read the press release for more information.

Face recognition algorithms

FRVT 1:1 and 1:N Ongoing

In 2023 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.

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.

Iris recognition algorithms

IREX 10 Results

In 2023 Neurotechnology's iris recognition algorithm has been judged by NIST as the most accurate among the IREX 10 participants in the Rank 1 category. The submitted algorithm outperformed other contenders in both single-eye and two-eye assessments. Also, it showed top results for most performance metrics.

Read the press release and our comments on IREX 10 participation for more details about the results.

IREX I, III, IV and IX Results

Over the time, Neurotechnology showed perfect results in the NIST IREX, IREX III, IREX IV and IREX IX evaluations.

Object recognition algorithms

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.

Research engineers from Neurotechnology teamed up and came in first place out of 2,293 teams who entered the Kaggle competition, which tasked top scientists and algorithm developers to design an AI fish detection and classification algorithm. A state-of-the-art deep neural networks based solution solved the problem and provided the best overall result in the competition.

Read the press release for more information.

Facebook icon   LinkedIn icon   Twitter icon   Youtube icon   Email newsletter icon
Copyright © 1998 - 2023 Neurotechnology