It uses a proprietary fingerprint matching algorithm that identifies fingerprints even if they are rotated, translated or have deformations.Īlso, the matching algorithm has a special mode for the cases when some fingerprint records have incorrect resolution. MegaMatcher is tolerant to fingerprint translation, rotation and deformation. Template generalization is used to generate a better quality template from several fingerprints.īetter quality templates result in a higher level of identification accuracy.This feature covers spoofing attempts performed with ecoflex, wood glue, latex and gelatin and is useful for fraud identification. MegaMatcher includes fingerprint image quality determination, which may be used during enrollment to ensure that only the best quality fingerprint template will be stored in the database.Ī deep learning based scanned fingerprint image classification is used to separate live/non-live fingerprints to detect finger presentation attack. The algorithm matches up to 200,000 flat fingerprint records per second on a single PC.
MegaMatcher allows flat-to-flat, flat-to-rolled or rolled-to-rolled fingerprint matching with a high degree of reliability and accuracy. Typically, conventional "flat" fingerprint identification algorithms perform matching between flat and rolled fingerprints less reliably due to the specific deformations of rolled fingerprints. The MegaMatcher fingerprint engine matches rolled and flat fingerprints between themselves.
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NIST has recognized MegaMatcher fingerprint algorithm as MINEX compliant and suitable for use in personal identity verification ( PIV) program applications.
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