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About fingerprint identification
Human fingerprints are unique to each person and can be regarded as a
sort of signature, certifying the person's identity. Because no two
fingerprints are exactly alike, the process of identifying a fingerprint
involves comparing the ridges and impressions on one fingerprint to
those of another.
This first involves capturing the likeness of the fingerprint, either
through use of a fingerprint scanner (which takes a digital picture of a
live fingerprint), scanning a pre-existing paper-based fingerprint image
or by pulling what is known as a "latent fingerprint" from a crime scene
or other place of investigation, from which a digital image is created.
Once the fingerprint image is captured, the process of identification
involves the use of complex algorithms (mathematical equations) to compare the
specific features of that fingerprint to the specific features of one or more
fingerprint images that have been previously stored in a database.
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Read more about:
PC-based:
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Technology
•
SDK
Embedded:
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Technology
•
EDK
Large-scale AFIS:
•
Technology
•
SDK
Extensions:
•
Smartcard Finger-Match add-on
• Template
management add-on
• WSQ support
add-on |
Fingerprint recognition technology
The most famous application of fingerprint recognition technology is in
criminology. However, nowadays, automatic fingerprint matching is becoming
increasingly popular in systems which control access to physical locations (such
as doors and entry gates), computer/network resources or bank accounts, or which
register employee attendance time in enterprises.
Straightforward matching of the to-be-identified fingerprint pattern against
many already known fingerprint patterns would not serve well, due to the high
sensitivity to errors in capturing fingerprints (e.g. due to rough fingers,
damaged fingerprint areas or the way a finger is placed on different areas of a
fingerprint scanner window that can result in different orientation or
deformation of the fingerprint during the scanning procedure). A more advanced
solution to this problem is to extract features of so called minutiae points
(points where the tiny ridges and capillary lines in a fingerprint have branches
or ends) from the fingerprint image, and check matching between these sets of of
very specific fingerprint features.
The extraction and comparison of minutiae points requires sophisticated
algorithms for reliable processing of the fingerprint image, which includes
eliminating visual noise from the image, extracting minutiae and determining,
rotation and translation of the fingerprint. At the same time, the algorithms
must be as fast as possible for comfortable use in applications with a large
number of users.
Many of these applications can run on a PC, however some applications require
that the system be implemented on low cost, compact and/or mobile embedded
devices such as doors, gates, handheld computers, cell phones etc.). For
developers who intend to implement the fingerprint recognition algorithm into a
microchip, compactness of algorithm and small size of required memory may also
be important.
Related Products
VeriFinger Software Development Kit (SDK):
In 1998 Neurotechnology developed
VeriFinger , a fingerprint identification algorithm, designed for biometric
system integrators. Since that time, the company has released 12 algorithm
versions, with the current version, VeriFinger 6.0, providing the most powerful
fingerprint recognition algorithms to date.
VeriFinger SDK is offered for a competitive price and developers can select
from these types of SDK:
- VeriFinger 6.0 Standard SDK is intended for PC-based
biometrical application development. It includes
Matcher and
Extractor
components, programming samples and tutorials, fingerprint scanner drivers
and software documentation. The SDK allows the development of biometric
applications for Microsoft Windows, Linux or Mac OS X operating systems.
- VeriFinger 6.0 Extended SDK is intended for biometrical
Web-based and network application development. It includes
all features of Standard SDK. Additionally, the SDK contains sample client
applications, tutorials and a ready-to-use matching server.
Buy VeriFinger Standard SDK Now
Buy VeriFinger Extended SDK Now
FingerCell Embedded Development Kit (EDK):
Neurotechnology's
FingerCell embedded fingerprint identification technology was developed on
the VeriFinger basis and adapted for use in low cost, compact and/or mobile
embedded devices such as doors, gates, handheld computers and cell phones.
FingerCell 2.1 EDK is available on 30 day trial period.
This downloadable
trial kit allows developers to explore the technology and to try it in real
environments and real applications.
Buy FingerCell Library EDK Now
MegaMatcher Software Development Kit (SDK):
MegaMatcher is a multi-biometrical technology, intended for large-scale
face-fingerprint systems and AFIS integrators. The technology includes
fingerprint and facial recognition engines that could be used either separately
or together. The fingerprint engine's performance and reliability has been
acknowledged by NIST MINEX.
MegaMatcher 2.0 SDK includes server software for local multi-biometrical
systems, cluster software for large-scale multi-biometrical products
development, and a set of valuable task-specific components.
Buy MegaMatcher Standard SDK Now
Buy MegaMatcher Extended SDK Now
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