VeriFinger Embedded algorithm features and capabilities

VeriFinger Embedded fingerprint identification algorithm is intended for embedded and mobile biometric system integrators. The VeriFinger Embedded 1.1 technology is a port of VeriFinger 6.5 technology for ARM-based processors.

VeriFinger Embedded provides AFIS quality fingerprint recognition on embedded and mobile devices. The technology is NIST MINEX compliant, as it is based on the MegaMatcher fingerprint identification engine that has been acknowledged by NIST as suitable for use in personal identity verification (PIV) program applications.

The VeriFinger Embedded algorithm follows the commonly accepted fingerprint identification scheme, which uses a set of specific fingerprint points (minutiae) along with a number of proprietary algorithmic solutions that enhance system performance and reliability. Some are listed below:

• Rolled and flat fingerprints matching. The VeriFinger Embedded algorithm matches flat-to-rolled, flat-to-flat or rolled-to-rolled fingerprints with high reliability, as it is tolerant to fingerprint deformations. Rolled fingerprints have much bigger deformation due to the specific scanning technique (rolling from nail to nail) than those scanned using the "flat" technique. Conventional "flat" fingerprint identification algorithms usually perform matching between flat and rolled fingerprints less reliably due to the mentioned deformations of rolled fingerprints.
• Tolerance to fingerprint translation, rotation and deformation. VeriFinger Embedded proprietary fingerprint template matching algorithm is able to identify fingerprints even if they are rotated, translated, deformed and have only 5 - 7 similar minutiae (usually fingerprints of the same finger have 20 - 40 similar minutiae).
• Identification capability. VeriFinger Embedded functions can be used in 1-to-1 matching (verification), as well as 1-to-many mode (identification).
• Image quality determination. VeriFinger Embedded is able to ensure that only the best quality fingerprint template will be stored into database by using fingerprint image quality determination during enrollment.
• Adaptive image filtration. This algorithm eliminates noises, ridge ruptures and stuck ridges for reliable minutiae extraction – even from poor quality fingerprints – with a processing time of less than 1 second.
• Features generalization mode. This fingerprint enrollment mode generates the collection of generalized fingerprint features from a set of fingerprints of the same finger. Each fingerprint image is processed and features are extracted. Then the features collection set is analyzed and combined into a single generalized features collection, which is written to the database. This way, the enrolled features are more reliable and the fingerprint recognition quality considerably increases.

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Technical Specifications

500 dpi is the recommended fingerprint image resolution for VeriFinger Embedded. The minimal fingerprint image resolution is 250 dpi.

All fingerprint templates should be loaded into RAM before identification, thus the maximum fingerprint templates database size is limited by the amount of available RAM.

A Java application based on VeriFinger Embedded 1.1 technology is able to process a fingerprint image in less than 1 second.

Fingerprint record size in a template is configurable and can take from 200 bytes to 6 kilobytes. Multiple fingerprint records can be stored in the template.

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Reliability Test Results

We present the testing results to show the VeriFinger Embedded 1.1 algorithm's reliability evaluations. The reliability tests were performed with images from different class fingerprint readers.

Three tests were performed during each experiment:

• Test 1 maximized matching accuracy. VeriFinger Embedded 1.1 algorithm reliability in this test is shown as blue curves on the ROC charts.
• Test 2 maximized matching speed. VeriFinger Embedded 1.1 algorithm reliability in this test is shown as green curves on the ROC charts.
• Test 3 minimized template size. VeriFinger Embedded 1.1 algorithm reliability in this test is shown as red curves on the ROC charts.

Receiver operation characteristics (ROC) curves are usually used to demonstrate the recognition quality of an algorithm. ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR). Note that VeriFinger Embedded 1.1 fingerprint template matching engine is a port of VeriFinger 6.5 PC-based engine for ARM-based processors, thus the reliability testing results and the ROC curves for both engines are the same.

Experiment 1 (DigitalPersona U.are.U 4000) Click to zoom

Experiment 2 (Futronic FS80) Click to zoom

Experiment 3 (SONATEQ FDB1-75TS1) Click to zoom

VeriFinger fingerprint identification algorithm versions consistently have shown some of the best results for reliability in several biometric competitions, including the International Fingerprint Verification Competition (FVC2006, FVC2004, FVC2002 and FVC2000) and the National Institute of Standards & Technology (NIST) Fingerprint Vendor Technology Evaluation (FpVTE 2003), where Neurotechnology ranked among the top five companies for accuracy in single-finger tests.

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