VeriFinger algorithm features and capabilities

All performance tests were made on Intel Core2 processor with 4 cores running at 2.66 GHz.

In 1998 Neurotechnology developed VeriFinger, a fingerprint identification algorithm designed for biometric system integrators. Since that time, Neurotechnology has released more than 10 versions of the VeriFinger algorithm, providing the most powerful fingerprint recognition algorithms to date.

The latest VeriFinger 6.2 version is NIST MINEX compliant, as it is based on the MegaMatcher fingerprint identification engine that has been certified by NIST for use in personal identity verification (PIV) program applications.

The VeriFinger 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 algorithm matches flat-rolled, flat-flat or rolled-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's 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) and matches 3,000 - 56,000 flat fingerprints per second (see technical specifications for more details).
• Faster matching using pre-sorted database entries. For some identification tasks VeriFinger's effective matching speed can be increased to 15,000 - 70,000 fingerprints per second (on one processor core) by pre-sorting database entries using certain global features. Fingerprint matching is performed first with the database entries having global features most similar to those of the test fingerprint. If matching within this group yields no positive result, then the next record with most similar global features is selected, and so on, until the matching is successful or the end of the database is reached. In most cases there is a fairly good chance that the correct match will be found at the beginning of the search. As a result, the number of comparisons required to achieve fingerprint identification decreases drastically, and correspondingly, the matching speed increases. See technical specifications for more details.
• Identification capability. VeriFinger functions can be used in 1-to-1 matching (verification), as well as 1-to-many mode (identification).
• Image quality determination. VeriFinger 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 0.1 - 0.2 seconds. A screenshot of the VeriFinger demo application shows an initial flat fingerprint image (left window), and the same image after the noise filtering and processing by VeriFinger (right window), with minutiae positions and directions marked by red circles and lines.
• 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.
• Scanner-specific algorithm optimizations. VeriFinger 6.2 includes algorithm modes that help to achieve better results for the supported fingerprint scanners.

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Technical Specifications

All specifications are given for Intel Core2 processor with 4 cores running at 2.66 GHz

500 dpi is the recommended fingerprint image resolution for VeriFinger. 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.

The table below shows the technical specifications of VeriFinger 6.2 algorithm. The algorithm's performance depends on fingerprint scanner that was used for collecting fingerprint images, thus the specifications are given for two groups of flat fingerprint scanners:

• Biometric scanners in these specifications are scanners that produce fingerprint images of about 300 x 300 pixels. These scanners are usually compact and inexpensive. An example of biometric scanner is DigitalPersona U.are.U 4000.
• AFIS-class scanners in these specifications are flat fingerprint scanners that have at least 1" x 1" fingerprint sensors and produce fingerprint images of at least 500 x 500 pixels or even larger images. These scanners are mostly intended for use in large-scale AFIS projects that need to collect high quality fingerprint images. An example of AFIS-class scanner is Cross Match Verifier 300

VeriFinger fingerprint template matching algorithm can use more than one processor core on multi-core processors.

(1) For databases with 500 or more fingerprints. Use with smaller sample fingerprint databases typically yields lower speed.

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Reliability and Performance Test Results

All tests were performed on Intel Core2 processor with 4 cores running at 2.66 GHz.

Digital Persona
U.are.U 4000

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Cross Match
Verifier 300 LC

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We present the testing results to show how VeriFinger 6.2 technical specifications correspond the practical algorithm's performance and reliability evaluations.

Flat fingerprint databases were collected with two fingerprint scanners for algorithm testing:

• DigitalPersona U.are.U 4000. 1,400 fingerprint images were collected using this scanner, with image size 318 x 330 pixels.
• Cross Match Verifier 300 LC. 1,600 fingerprint images were collected using this scanner, with image size 504 x 480 pixels.

Three tests were performed with each database:

• Test 1 maximized matching accuracy. VeriFinger 6.2 algorithm reliability in this test is shown as red curves on the ROC charts.
• Test 2 maximized matching speed. VeriFinger 6.2 algorithm reliability in this test is shown as green curves on the ROC charts.
• Test 3 minimized template size. VeriFinger 6.2 algorithm reliability in this test is shown as blue curves on the ROC charts.

Template matching was performed using all 4 cores of the processor.

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). Charts with ROC curves for both databases are available on the right.

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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