More Than 25 Years of Independent Biometric Performance Testing
Every Evaluation, Grouped by Modality
Every fingerprint, palmprint, face, iris and voice algorithm submitted to NIST, the University of Bologna, UIDAI and IJCB – grouped by modality, in order.
- 26 years of continuous testing
- 20 first-place results
- 13 evaluations still active
- 5 modalities evaluated
VeriFinger since 1998: verification, large-scale identification, interoperability, slap segmentation and latent prints.
Fingerprint
VeriFinger achieved the best reliability results among all participants.
The first Fingerprint Verification Competition, entered under the company's former name, Neurotechnologija.
Marked the beginning of participation in independently organized fingerprint algorithm competitions.
One silver and two bronze medals across the evaluated datasets and measures.
Continued participation with a fingerprint recognition algorithm across standardized FVC datasets.
One of the strongest reliability results in the Middle Scale Test.
First NIST fingerprint evaluation, entered as Neurotechnologija.
FpVTE assessed one-to-many identification and gave an early independent measure of suitability for larger databases.
VeriFinger took four gold in the Open Category; FingerCell six silver in the Light Category.
VeriFinger: four gold, three silver and two bronze medals in the Open Category.
FingerCell, for embedded and lower-resource platforms: one gold, six silver and three bronze in the Light Category.
The two categories demonstrated fingerprint algorithms for both full-scale systems and constrained platforms.
The P058 algorithm ranked highest overall in the Open Category by Average Zero FMR.
Four gold, two silver and two bronze medals in the Open Category.
A separate algorithm ranked second in the Light Category by Average Zero FMR, taking one gold and four bronze medals.
Zero FMR measures performance where false acceptance is reduced to zero – the operating point that matters for high-security deployments.
Recognized MINEX compliant for both template generation and matching.
MINEX tests the interoperability of fingerprint templates created and matched by different implementations.
Relevant wherever biometric records must stay usable across equipment, software versions or organizations.
WSQ 3.1 Certification
Seven FBI certifications, one for each supported computing platform.
Ongoing MINEX
Placed second in the Ongoing MINEX ranking.
Ranked fourth for overall accuracy; four times more accurate than the second-fastest algorithm in Class C.
MegaMatcher SDK and MegaMatcher Accelerator were evaluated on single fingers, flat tenprints and complex tenprint records.
The Class C result was measured on 5 million tenprints under a 90-second search limit.
Entered the ongoing MINEX III template interoperability program.
MINEX III remains active, so results may change as participants submit updated algorithms.
MegaMatcher On Card passed MINEX III testing.
MINEX III is the ongoing evaluation of fingerprint template interoperability for the US PIV program.
MINEX compliance is often a mandatory requirement in public tenders in the United States and elsewhere.
MINEX III: 2017 Results
Most interoperable matcher, and fourth most accurate native template matcher.
Submission 4E ranked among the most accurate algorithms across all 33 experiments.
Second fastest during enrollment and second or third fastest in template matching.
PFT II evaluated one-to-one verification with proprietary templates on datasets of up to 120,000 subjects; the program ended in 2019.
MINEX III: 2018 Results
First for template generator interoperability, second for matching.
Took the top position on entry to the program.
PFT III measures one-to-one fingerprint verification using proprietary templates, with successive algorithm versions submitted over time.
MINEX III: Template Generator
First place in the template generator category.
Evaluated for separating individual fingerprints from four-finger slap captures.
Published results show tradeoffs between speed and accuracy; the submission was neither the fastest nor the most accurate across the four reported categories.
Slap segmentation matters in enrollment systems that capture four-finger slaps – national ID, voter registration, border control and criminal registration workflows.
Top result in the standard fingerprint verification benchmark.
FVC-onGoing accepts new algorithms continuously while keeping the evaluation datasets fixed, so results stay comparable over time.
Top result in the hard-conditions verification benchmark.
FV-HARD uses the same protocol as FV-STD but on more difficult image conditions.
PFT III: Position Defended
Top position confirmed in the 2022, 2023, 2024 and 2025 releases.
MINEX III: Smart-Card Compliance
Smart-card submission 020A confirmed MINEX III compliant.
VeriFinger 13.1 ranked first among the valid submissions.
Highest area under the ROC curve at 0.854, with an equal error rate of 0.228.
Failure-to-enroll fell to 6.2% from 40.8% for the VeriFinger 12.3 baseline – not the best FTE in the competition.
Lowest computational cost of all submissions: 0.78 seconds per fingerprint pair.
Highest accuracy across most of the evaluated datasets.
December 2024 results: highest accuracy on most evaluated datasets and among the leaders for feature extraction speed.
ELFT measures latent friction ridge identification – marks recovered from crime scenes searched against ten-print and palm databases.
First place for fingerprint recognition out of 2,106 applications.
Strongest one-to-one matching performance among the three submissions selected for final evaluation.
Used a longitudinal dataset: samples from children aged 5–10, recaptured 5–10 years later, testing age invariance.
First in the majority of test categories, including index-finger and multi-finger identification.
Successor to FpVTE 2012 for large-scale one-to-many fingerprint identification.
Neurotechnology+0106 tied for first in Class A index-finger identification and achieved zero errors in four experiments.
Class B: second of 11 on individual slaps. Class C: second in ten-finger identification.
Against 5 million records, median search times were 613 ms plain and 560 ms rolled, with zero API timeouts.
Participated in both the recognition and the new quality-assessment tracks.
The second edition split the evaluation into latent fingerprint recognition and latent fingerprint quality assessment.
Neurotechnology co-authored the publication presenting the competition and its results.
[INTERNAL CONFIRMATION REQUIRED: Add Neurotechnology's exact 2025 recognition and quality-assessment placements after checking the complete IJCB paper. Until then, state participation only; the results are published, but the company's placement has not been confirmed from the paywalled paper.]
Fastest performance and near-best accuracy in most categories.
A top performer in the 2025 SlapSeg III results across most evaluated categories.
[INTERNAL CONFIRMATION REQUIRED: This statement conflicts with the SlapSeg III entry above, which reports that the submission was neither the fastest nor the most accurate across the four reported categories. Confirm which result release each statement describes before publication.]
First place in both identification and investigation scenarios.
First in both scenarios on the IARPA N2N and FBI-Provided Solved #1 datasets.
Leading positions on the DoD-Provided Dataset #1 and shared first place on the FBI Laboratory dataset.
Most interoperable PIV Level 1 generator and most accurate native Level 2 generator and matcher pair.
Also the leading interoperable Level 1 generator and matcher pair.
Highest accuracy, alongside a second submission tuned for speed and template size.
One submission achieved the highest accuracy; another emphasized faster matching and smaller templates.
No entries match the current filter.
Independently benchmarked palmprint recognition.
Palmprint
FVC-onGoing Palmprint Benchmark
First place in the palmprint recognition benchmark.
No entries match the current filter.
VeriLook in verification, identification, video, paperless travel, masked faces, image quality, morphing and presentation-attack detection.
Face
Among the top eight most accurate algorithms out of 16 vendors.
An experimental face engine integrated with a face tracker, tested on passively recorded non-cooperating subjects.
Findings later contributed to improvements in VeriLook SDK and MegaMatcher SDK.
FRVT 1:N Identification
Sixth of 40 participants at 12 million subjects; third of 40 on images captured up to 18 years after enrollment.
Evaluated in biometric boarding and airport security gallery simulations.
Tested one-to-many identification against galleries built from passport, visa or previous travel images.
Among the leading algorithms in border-control, kiosk, frontal and profile mugshot scenarios.
Published 13 January 2022, covering both one-to-one verification and one-to-many identification.
In July 2023 FRVT was split into FRTE and FATE, and participation continued in both tracks.
Leading accuracy in supervised and unsupervised visa, border and kiosk scenarios.
Published 27 November 2023.
FRTE 1:1 measures one-to-one face verification across a range of operational image sources.
Strong results in civil identification and investigation scenarios.
Includes both frontal and profile mugshot matching.
Top results for recognizing masked and otherwise occluded faces.
Published 20 December 2023.
Measures the accuracy cost of lower-face occlusion, which became an operational requirement for many deployments.
Facial image quality assessment tested on challenging photographic conditions.
Quality estimation matters because enrollment and matching accuracy depend on pose, focus, lighting, resolution and facial visibility.
Evaluated for detection of face morphing attacks.
Tests detection of images combining the features of two or more people, which could otherwise be used by more than one person.
Relevant to document issuance and identity fraud prevention.
Face presentation-attack detection certified at Level 2.
An independent certification, separate from NIST FATE PAD – it should not be presented as participation in that NIST evaluation.
Neurotechnology's face liveness check algorithm was tested by BixeLab and iBeta – BixeLab issued the Level 2 letter of confirmation, and iBeta separately issued a Level 1 conformance letter. [INTERNAL CONFIRMATION REQUIRED: New copy added to preserve the iBeta fact after this entry was reattributed to BixeLab. The laboratory-to-level attribution (BixeLab Level 2, iBeta Level 1) is taken from the public certificates page, not from the letters themselves; confirm it before publication. No date is stated for the iBeta Level 1 letter because none has been read from the letter.]
DMSTI-Neurotechnology ranked first in the Full Data Track.
A research collaboration with DMSTI, the Institute of Data Science and Digital Technologies at Vilnius University.
Adapted CLIP ViT-L/14 on approximately 3 million synthetic face images across 35,000 identities, using full fine-tuning with a Sub-Center ArcFace head.
Highest average accuracy on small-scale benchmarks at 95.51%, leading IJB-B and IJB-C at all reported FAR thresholds.
87.42% TAR at FAR 10−5 on IJB-C versus 76.71% for the FRoundation baseline; led RFW at 91.70% average accuracy with the lowest standard deviation, 2.33.
No entries match the current filter.
VeriEye through NIST IREX I, III, IV, IX and 10.
Iris
VeriEye 2.1 ranked among the top three for accuracy, 25–75× faster than competitors.
Templates were 3.5–7.5× smaller than those of competing algorithms.
Established the starting point for independently evaluated iris recognition development.
Second-fastest matcher, with three times the accuracy of the only faster contender.
NIST evaluated more than 86 algorithms and variations from nine companies and two universities.
IREX III focused on performance and interoperability of compact iris templates.
The only submission returning results in under 0.5 s on a 1.6-million-iris gallery.
66 iris prototypes from 12 commercial and academic organizations were evaluated in a large-scale identification task.
Across all tests, submissions ranked among the top four participants for accuracy.
Ranked second for accuracy; the accelerated version was nearly 50× faster than any other matcher.
NIST evaluated 46 iris recognition algorithms from 13 commercial and research organizations.
Joined the ongoing large-scale iris identification evaluation.
IREX 10 accepts submissions continuously and measures one-to-many iris identification at scale.
Judged the most accurate participant in the Rank 1 category.
Outperformed other contenders in both single-eye and two-eye assessments.
Top results across most of the reported performance metrics.
Ranked first in all IREX 10 categories, single-eye and two-eye alike.
Lowest Rank 1 miss rate in both two-eye and single-eye testing.
Lowest false negative identification rate at a false positive identification rate of 0.01.
Top results for both automated identification and Rank 1 investigation-style accuracy.
No entries match the current filter.
VeriSpeak speaker recognition in the NIST SRE series. Participation only – no ranking claims permitted.
Voice
Participated in an evaluation series NIST has run since 1996.
SRE24 assessed automated person detection using conversational telephone speech and audio from video.
Audio-only, visual-only and audio-visual tracks, with cross-source and cross-language comparisons.
NIST names participating teams but does not link them to scores, and restricts promotional claims about standing.
No entries match the current filter.
About These Results
Participation in an evaluation and publication of results do not constitute endorsement by NIST, UIDAI or any other evaluating organization. NIST SRE rules prohibit advertising a participant's standing, so voice results are stated as participation only.
Related
Technology Awards and Press Releases
Top results in recent NIST ELFT evaluation with the newest Neurotechnology's latent fingerprint recognition algorithm submission.
First place in NIST PFT III evaluation. Once again, Neurotechnology's latest proprietary fingerprint verification algorithm has outperformed all the contenders.
First place overall secured in NIST MINEX III evaluation, in 2023. The template matcher sets a new level of accuracy.
Besides the top achievements, mentioned in the press releases above, Neurotechnology showed excellent results in other biometric technology evaluations and competions:
- SlapSeg III Evaluation – in 2025 Neurotechnology's slap fingerprint segmentation algorithm showed off as a top performer, featuring the fastest performance and near-best accuracy in most categories of the evaluation.
- IREX 10 – in 2023 Neurotechnology's iris recognition algorithm has been judged by NIST as the most accurate among the 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.
- FVC-onGoing – in 2019 Neurotechnology's palmprint matching algorithm has shown the top result at the FVC-onGoing evaluation.
- FRTE 1:1 Verification
- FRTE 1:N Identification
- FRTE Face Mask Effects
- FATE Quality
- FATE MORPH
Applications
Neurotechnology provides solutions, based on NIST-proven biometric algorithms, which combine high performance with top reliability. These solutions may be applied in various industries, as well as utilized for both civil and forensic applications, which requre properly-tested, accurate technologies.
Creates secure identity registers, preventing identity fraud and ensuring accurate distribution of benefits.
Biometric technologies ensure fair and secure elections due to accurate voter management capabilities.
Facial recognition and fingerprint scanners verify traveler identity and prevent unauthorized entry.
Biometric technologies are used for criminal identification, investigation and tracking.
Biometric authentication enhances security and streamlines transactions by replacing traditional passwords.
help you achieve your biometric data management goals.
