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  VeriFinger Standard SDK


 
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VeriFinger SDK - Standard Edition

Fingerprint identification for PC and Web solutions

Click Here to download the full VeriFinger SDK brochure as a .pdf document

VeriFinger is a fingerprint identification technology intended for biometric systems developers and integrators. The technology assures system performance with fast, reliable fingerprint matching in 1-to-1 and 1-to-many modes.

VeriFinger is available as a software development kit that allows development of PC- and Web-based solutions on Microsoft Windows, Linux and Mac OS X platforms.

Advantages of VeriFinger

  • 1000+ end-user product brands in 98 countries have been using the VeriFinger algorithm the past 12 years.
  • Full NIST MINEX certification and Fingerprint Verification Competition (FVC) awards since 2000.
  • Fast rolled and flat fingerprint matching that is tolerant to fingerprint translation, rotation and deformation.
  • Compact fingerprint template and unlimited database size.
  • Available as multiplatform SDK that supports multiple scanners and multiple programming languages.
  • Reasonable prices, flexible licensing and free customer support.

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 6.3 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 the Technical Specs tab 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 the Technical Specs tab 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 6.3 includes algorithm modes that help to achieve better results for the supported fingerprint scanners.
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