Technology Awards

• MINEX certification
• FVC2006 results
• FpVTE 2003 results
• FVC2004, FVC2002 and FVC2000 results
• Comments on competitions' results

MINEX Certification

In 2007 MegaMatcher 2.0 fingerprint technology received full MINEX Certification. NIST certified MegaMatcher for use in personal identity verification program applications.

The Minutiae Interoperability Exchange Test (MINEX) evaluates fingerprint template encoding and matching to determine compliance with the government's Personal Identity Verification (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.

MegaMatcher 2.0 is one of only 12 algorithms worldwide to receive full MINEX certification for both fingerprint template encoding and matching. This certification puts MegaMatcher 2.0 SDK into the U.S. government buyers' certified list of fingerprint recognition algorithms.

See also our press release.

Fingerprint Verification Competition (FVC2006)

Neurotechnologija 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."

Neurotechnologija 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 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:

• EER (Equal Error Rate) – where the False Acceptance Rate (FAR) is equal to the False Rejection Rate (FRR),
• FMR 100 (FRR at the FAR=1% level),
• FMR 1000 (FRR at the FAR=0.1% level),
• Zero FMR (FRR at the FAR=0% level).

When considering the results of competitions, it is important to put the competition criteria into the perspective of real-world biometrical 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 "bad guys" in. Thus, most real-world applications of biometric technology are set to have a low FAR. Most real applications set the FAR as close to zero as possible. A FAR=0.001% is common and sometimes FAR=0.0001% 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 represent reliability in 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

Neurotechnologija's algorithm achieved one of the best reliability results in the Middle Scale Test among FpVTE 2003 participants:

• In real-world scenarios, Neurotechnologija's algorithm would show even higher accuracy levels.
• See FpVTE web site for a detailed report of the evaluation results^*.

^* Results shown from the NIST FpVTE 2003 do not constitute 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)

Neurotechnologija's algorithms consistently showed some of the best reliability results among participants, earning the following awards:

• FVC2004 (See FVC2004 web site for details)
  • Open Category: four gold, three silver and two bronze medals for the VeriFinger algorithm
  • Light Category: one gold, six silver and three bronze medals for the FingerCell algorithm
• FVC2002 (See FVC2002 web site for details)
  • One silver and two bronze medals
• FVC2000 (See FVC2000 web site for details)
  • VeriFinger algorithm showed the best reliability results among all participants.

Since the FpVTE 2003 and FVC2004 competitions were held, Neurotechnologija 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 competitions' results

The FpVTE protocol was strict and did not allow using 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 certain 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 (1:1) but not identification (1:N) results. One of the strongest capabilities of Neurotechnologija's algorithms is fast identification, therefore a 1:N 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.