{"id":3684,"date":"2022-12-02T08:36:47","date_gmt":"2022-12-02T07:36:47","guid":{"rendered":"https:\/\/innovatrics.com\/trustreport\/?p=3684"},"modified":"2023-02-28T16:01:09","modified_gmt":"2023-02-28T15:01:09","slug":"minex-benchmarks-potential-for-improving-elections-everywhere","status":"publish","type":"post","link":"https:\/\/innovatrics.com\/trustreport\/minex-benchmarks-potential-for-improving-elections-everywhere\/","title":{"rendered":"Fast, compact and standardised: the Minex benchmark\u2019s potential for improving elections everywhere"},"content":{"rendered":"\n
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Contributor<\/h4>\n\n\n
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Jakub Sochor<\/span><\/h3>\n

AI Solution Expert at Innovatrics<\/p>\n <\/div>\n<\/div><\/div>\n\n\n\n

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Imagine being in charge of building a list of all eligible voters in your home country from scratch \u2013 a task crucial for preserving democracy. Yet there is almost no starting point of reference because the citizen registry is incomplete and riddled with duplicates, minors or deceased people. You and your team opt for biometrics, hoping the voter registry you create can later become a strong base for building a robust eGovernment system. But what is the twist?<\/strong><\/p>\n\n\n\n

After travelling the country and dutifully collecting biometric data on millions of eligible voters, you find out that you need to use a different biometric provider. Things like this happen. However, you\u2019ve been collecting data such as fingerprints in the original provider\u2019s proprietary format, which the other provider is unable to read. <\/p>\n\n\n\n

So how could you do better next time? By making sure that you work with providers who standardise biometric data according to the Minex benchmark.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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More about the NIST MINEX III<\/strong><\/p>\n\n\n\n

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The Minutiae Interoperability Exchange (MINEX) III<\/a> benchmark for fingerprint templates was developed by NIST and ranks among the most relevant tests in the industry. <\/p>\n\n\n\n

NIST’s Minex III evaluates algorithms submitted for template generation and template matching. Each participant submits their own template generator and, optionally, their own template matcher.<\/p>\n\n\n\n

Governments, organisations and private companies all rely on NIST’s benchmarks to define requirements for biometric technology projects.<\/strong><\/p>\n<\/div>\n\n\n\n

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Minex is a test developed by the National Institute of Standards and Technology (NIST) that ranks all the fingerprint verification algorithms that have been submitted for testing by their biometrics providers. If an algorithm achieves a high ranking in Minex, it means that it is superior in ensuring accurate communication between two or more different biometric systems. <\/strong>A basic example of such a system is ATMs <\/strong>\u2013 each bank has its own biometric database, yet needs to verify the identities of customers from other banks in order to authorise money withdrawals.<\/p>\n\n\n\n

Innovatrics has been submitting fingerprint verification algorithms to the Minex benchmark for a long time, continually ranking among the top three performers with regards to its type of algorithm. Indeed, there is more than one algorithm needed in order to collect, and later verify, the identity of those enrolled. Let\u2019s briefly discuss how it\u2019s done. <\/p>\n\n\n\n

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How to get two biometric systems to communicate<\/h2>\n\n\n\n

If two different biometric systems exchange information, the information often has to come in a standardised format. For example, for one system to be able to verify fingerprints that were collected by a second system, there needs to be a standardised fingerprint template that is readable by both systems. <\/p>\n\n\n\n

Before going any further, it is important to understand the full process of biometric registration and verification. To enable biometric registration, a person first needs to provide their personal information plus biometric data \u2013 often a fingerprint scan. All this provided data is then stored in a database. During the process of verification, the person then provides their personal information again, which is subsequently checked against the available data in the database. If they match, the verification is approved.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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In order to test the algorithms, Minex therefore tests and rates two distinctive processes. First, how well one algorithm generates a standardised template from a collected scan of a fingerprint. And second, how reliably the second algorithm compares the already existing template to a new input and deems whether it belongs to the same person. <\/p>\n\n\n\n

Biometrics providers, including Innovatrics, usually submit two algorithms to the Minex benchmark to prove their level of interoperability. These are called generator <\/strong>and matcher <\/strong>algorithms. The goal of the generator algorithm is to generate templates from fingerprint scans, whilst the matcher is designed to match a newly collected scan to the existing template in the database. Put simply, the generator wants to create templates so precise that when the same person scans their fingerprints again, the matcher will have zero problems recognising if the person is who they say they are. <\/p>\n\n\n\n

For example, if the country in the voter-registry story above standardised their citizens\u2019 data, they would start by generating templates from the fingerprints they collected all over the country. Then, on election day, eligible citizens coming to vote would be verified by a fingerprint scan using the matcher algorithm. The exchange between the two biometric providers would be smooth and painless.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Innovatrics has been ranked #1 in the matcher interoperability for a long time.<\/strong> As of September 2022, the company received new matcher test scores, confirming its position as the fastest and most precise algorithm in the world.<\/strong> Meanwhile, Innovatrics\u2019 template generator also continues to be among the fastest algorithms worldwide.<\/p>\n<\/div><\/div>\n\n\n\n

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How to standardise a fingerprint template<\/h2>\n\n\n\n

Although the details behind Minex’s purpose can get complicated, rumour has it that it actually came from a very simple reason: access cards for the company’s employees. The staff at NIST wanted to use fingerprint scans to get into the building, instead of having to remember and manually input an access code. The problem was, if they then had to change the finger-scanning device at the entrance at some point, the new one would likely have a hard time understanding the proprietary fingerprint database of the old device \u2013 which could result in employees potentially being stuck outside, unable to enter the building, because the system wouldn\u2019t let them in. If they wanted to avoid this, they needed to find a way to standardise the fingerprint scans. 
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A fingerprint gets standardised like this: the algorithm looks at the fingerprint scan and finds the so-called minutiae points<\/em> \u2013 where the papillary lines end or split (this is also called bifurcation). It is the location and orientation of the minutiae points that determine the uniqueness of a fingerprint.<\/p>\n\n\n\n

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 The green circles demonstrate where the papillary lines end while the red circles indicate where they split. The tails behind every circle indicate the orientation of the papillary lines.<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n
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Where else are Minex-benchmarked algorithms used?<\/strong><\/p>\n\n\n\n

The golden rule is to use standardised templates in all instances where one provider has to verify biometric data from a different system.<\/strong> It covers areas like:<\/p>\n\n\n\n