Verifinger-Fingerprint identification for stand-alone or client-server solutions

Available as a software development kit that allows development of stand-alone and network-based solutions on Microsoft Windows, Linux, macOS, iOS and Android platforms.

Features and Capabilities

1500+ end-user product brands in 100+ countries used the VeriFinger algorithm over the past 25 years.

Technology awards in NIST MINEX, PFT III, SlapSeg III, FpVTE and FVC evaluations.

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.

FingerCell SDK for embedded platforms, like microcontrollers, optionally available.

Reasonable prices, flexible licensing and free customer support.

Rolled and flat fingerprints matching. 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. 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 up to 40,000 flat fingerprints per second (see technical specifications for more details). Also, the matching algorithm has a special mode for matching different scale fingerprint records, as well as optional matching of mirrored fingerprints.

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. The image quality determination can tell if a finger is too wet, too dry, pressed too much or not enough, or if only tips of fingers are present.

Spoof fingerprint detection. A deep learning based scanned fingerprint image classification is used to separate live/non-live fingerprints to detect finger presentation attack. This feature covers spoofing attempts performed with ecoflex, wood glue, latex and gelatin and is useful for fraud identification.

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 0.6 seconds.

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.

Compact fingerprint template. VeriFinger allows to configure the number and size of fingerprint features in a fingerprint template. Combined with unlimited database size, this capability allows to optimize target system size and performance.

Scanner-specific algorithm optimizations. VeriFinger SDK includes algorithm modes that help to achieve better results for the supported fingerprint scanners.