Product Profile
Neurotechnology VeriFinger Algorithm
Neurotechnology VeriFinger Algorithm
  • Supplier: Neurotechnology
  • Region: Lithuania
  • Updated: 07/20/2009
  • Click Frequency: 1851
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Product Specifications
  • Features:
    * 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 5,000 – 14,000 flat fingerprints per second (when fingerprint image size is 300 x 300 pixels).
    * 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 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.
    * Identification capability. VeriFinger functions can be used in 1-to-1 matching (verification), as well as 1-tomany 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 (for flat fingerprints).
    * 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 6.0 includes algorithm modes that help to achieve better results for the supported fingerprint scanners.