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In 1998 Neurotechnology developed VeriFinger,
a fingerprint identification algorithm, designed for biometric
system integrators. Since that time, Neurotechnology has released more than 10
algorithm versions, providing the most powerful fingerprint recognition
algorithms to date.
The latest VeriFinger 6.0 version is based on
MegaMatcher 2.0 technology.
Why VeriFinger?
- Full MINEX Certification. VeriFinger 6.0 technology is
based on MegaMatcher 2.0 fingerprint identification engine that has been
certified by NIST for use in personal identity verification program
applications.
- Reliability. Even earlier VeriFinger fingerprint
identification algorithm versions consistently have shown some of the best
results for reliability in several biometric competitions, including the
International Fingerprint Verification Competition (FVC2006, FVC2004,
FVC2002 and FVC2000) and the National Institute of Standards & Technology
(NIST) Fingerprint Vendor Technology Evaluation (FpVTE 2003), where
Neurotechnology ranked among the top five companies for accuracy in
single-finger tests. VeriFinger 6.0 provides reliability improvements over
these earlier versions.
- Fingerprint enrollment time is 0.2-0.4 sec., and
VeriFinger can match up to 40,000 fingerprints per second
in 1:N identification mode. To confirm these results with your data, please
try VeriFinger algorithm demo (see
section below).
- VeriFinger matches rolled and flat fingerprints between
themselves. Usually conventional "flat" fingerprint identification
algorithms perform matching between flat and rolled fingerprints less
reliably due to the specific deformations of rolled fingerprints. VeriFinger
allows matching of flat-flat, flat-rolled or rolled-rolled fingerprints with
high reliability.
- Both face and fingerprint recognition technologies from the same
vendor. Compatible product interfaces and customer support from the
same source allow simple multi-biometric system integration
and help to achieve high system recognition quality. The VeriFinger
algorithm can be used alone or together with other Neurotechnology's
biometrical algorithms.
- VeriFinger is offered for a
competitive price.
Developers can select from several types of SDK and licensing models. Each
of these kits and models is intended for specific needs, and developers
always can make an upgrade by paying the difference between the current and
more powerful SDK.
Algorithm
VeriFinger algorithm follows the commonly accepted fingerprint identification
scheme, which uses a set of specific fingerprint points (minutiae). However, it
contains many proprietary algorithmic solutions, which enhance the system
performance and reliability. Some of them are listed below:
- VeriFinger includes fingerprint image quality determination
which can be used during enrollment to ensure that only the best quality
fingerprint template will be stored into database.
- VeriFinger algorithm is able to match rolled fingerprints,
flat fingerprints, and also rolled with flat between themselves. Due to the
specific scanning technique (rolling from nail to nail) rolled fingerprints
usually have much bigger deformation than those scanned using the "flat"
technique. VeriFinger matches rolled fingerprints very well, as it is
tolerant to fingerprint deformations.
- The adaptive image filtration algorithm allows to
eliminate noises, ridge ruptures and stuck ridges, and extract minutiae
reliably even from poor quality fingerprints, with a processing time of
about 0.2 - 0.4 seconds (all times are given for a Pentium 4, 3 GHz
processor). You can look at the
screenshot of
the VeriFinger demo application showing an example of initial 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.
- VeriFinger functions can be used in 1:1 matching (verification), as well
as 1:N mode (identification).
- VeriFinger includes a fast template matching algorithm that is
tolerant to fingerprint translation, rotation and deformation.
VeriFinger's proprietary fingerprint matching algorithm allows it to match
up to 40,000 fingerprints per second and identify fingerprints even if they
are rotated, translated, deformated and have only 5 - 7 similar minutiae
(usually fingerprints of the same finger have 20 - 40 similar minutiae).
- VeriFinger can use database entries which were pre-sorted
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.
- VeriFinger has the fingerprint enrollment with features
generalization mode. This mode generates the collection of the
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.
- VeriFinger 6.0 includes algorithm modes that help to
achieve better results for the
supported fingerprint
scanners.
Reliability Test Results and Technical Specifications
|
VeriFinger has been tested using fingerprint sets from
many scanners. Usually algorithm's recognition quality is expressed by
receiver operation characteristics (ROC) curves, that show the
dependence of false rejection rate (FRR) on the false acceptance rate
(FAR). We present ROC curves obtained from the databases collected with
CrossMatch Verifier 300 and DigitalPersona U.are.U 4000 scanners. These
charts also compare VeriFinger 5.0 (red curve) and VeriFinger 6.0 (green
curve) algorithms reliability.
As can be seen from the ROC curves, VeriFinger 6.0 false rejection
rate is only 0.13% with fingerprints from CrossMatch Verifier 300 and
0.6% with fingerprints from DigitalPersona U.are.U 4000 at the false
acceptance rate of 0.001%. The other specifications of the algorithm are
presented below. These parameters are given for a PC with 3 GHz Pentium
4 processor.
| VeriFinger 6.0 algorithm
technical specifications |
| Required fingerprint resolution |
> 250 dpi
500 dpi recommended |
| Fingerprint processing time |
0.2 - 0.4 seconds |
| Matching speed * |
up to 40,000 fingerprints/second |
| Size of one record in the database
** |
150 bytes - 1.8 Kbytes
(configurable) |
| Maximum database size |
unlimited |
|
Digital Persona
U.are.U 4000
Click to zoom
Cross Match
Verifier 300 LC
Click to zoom |
VeriFinger has been tested using fingerprint sets from many scanners. Usually
algorithm's recognition quality is expressed by receiver operation
characteristics (ROC) curves, that show the dependence of false rejection rate (FRR)
on the false acceptance rate (FAR). We present ROC curves obtained from the
databases collected with CrossMatch Verifier 300 and DigitalPersona U.are.U 4000
scanners. These charts also compare VeriFinger 5.0 (red curve) and VeriFinger
6.0 (green curve) algorithms reliability.
As can be seen from the ROC curves, VeriFinger 6.0 false rejection rate is
only 0.13% with fingerprints from CrossMatch Verifier 300 and 0.6% with
fingerprints from DigitalPersona U.are.U 4000 at the false acceptance rate of
0.001%. The other specifications of the algorithm are presented below. These
parameters are given for a PC with 3 GHz Pentium 4 processor.
| VeriFinger 6.0 algorithm technical
specifications |
| Required fingerprint resolution |
> 250 dpi
500 dpi recommended |
| Fingerprint processing time |
0.2 - 0.4 seconds |
| Matching speed * |
up to 40,000 fingerprints/second |
| Size of one record in the database ** |
150 bytes - 1.8 Kbytes
(configurable) |
| Maximum database size |
unlimited |
* VeriFinger 6.0, for sufficiently
large databases (500 or more fingerprints). Use with smaller sample fingerprint
database, typically yields lower speed.
** Average fingerprint with image size 300 x 300 pixels.
These results were also confirmed by the tests performed by our customers.
Algorithm Demo
Click to zoom
|
The VeriFinger demo application for Microsoft Windows 2000/XP/2003/Vista can
be
downloaded for evaluation of the VeriFinger fingerprint recognition
algorithm. The application enrolls and identifies fingerprints from image file
or supported fingerprint scanner, and can calculate receiver operating curves
(ROC) with custom fingerprint databases. Internet connection is not required to
run the application.
Sample fingerprint images can be
downloaded for evaluation purposes.
|
VeriFinger 6.0 Standard SDK and Extended SDK trials are also available for
downloading.
Related Products:
These products are based on the VeriFinger algorithm:
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