VeriFinger 12.3 includes algorithm modes that help to achieve better results for the supported fingerprint scanners. Scanner-specific algorithm optimizations.VeriFinger allows to configure the number and size of fingerprint features in a fingerprint template.Ĭombined with unlimited database size, this capability allows to optimize target system size and performance. This way, the enrolled features are more reliable and the fingerprint recognition quality considerably increases.
Then the features collection set is analyzed and combined into a single generalized features collection, which is written to the database. This fingerprint enrollment mode generates the collection of generalized fingerprint features from a set of fingerprints of the same finger.Įach fingerprint image is processed and features are extracted. This algorithm eliminates noises, ridge ruptures and stuck ridges for reliable minutiae extraction – even from poor quality fingerprints – with a processing time of 0.6 seconds. This feature covers spoofing attempts performed with ecoflex, wood glue, latex and gelatin and is useful for fraud identification. 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.Ī deep learning based scanned fingerprint image classification is used to separate live/non-live fingerprints to detect finger presentation attack. VeriFinger functions can be used in 1-to-1 matching (verification), as well as 1-to-many mode (identification). 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 up to 40,000 flat fingerprints per second (see technical specifications for more details).Īlso, the matching algorithm has a special mode for the cases when some fingerprint records have incorrect resolution.
Tolerance to fingerprint translation, rotation and deformation.Rolled fingerprints have much bigger deformation due to the specific scanning technique (rolling from nail to nail) than those scanned using the "flat" technique.Ĭonventional "flat" fingerprint identification algorithms usually perform matching between flat and rolled fingerprints less reliably due to the mentioned deformations of rolled fingerprints. The VeriFinger algorithm matches flat-to-rolled, flat-to-flat or rolled-to-rolled fingerprints with a high degree of reliability and accuracy, as it is tolerant to fingerprint deformations. The VeriFinger algorithm is based on deep neural networks and 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. Numerous awards in competitions and technology evaluations, including FVC and FpVTE, have been received by VeriFinger. Since that time, Neurotechnology has released more than 10 major and minor versions of the VeriFinger, providing most powerful fingerprint recognition algorithms to date. In 1998 Neurotechnology developed VeriFinger, a fingerprint identification technology designed for biometric system integrators.
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Full NIST MINEX compliance, FpVTE and FVC awards since 2000.1500+ end-user product brands in 100+ countries used the VeriFinger algorithm over the past 24 years.
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