Read more about:
• VeriFinger technology and SDK
• Free Fingerprint Verification SDK
• MegaMatcher technology and SDK
• MegaMatcher On Card SDK
• VeriFinger Embedded SDK
Contents of this page
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.
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.
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 15 algorithm versions, with the current version, VeriFinger 6.6, providing the most powerful fingerprint recognition algorithms to date. VeriFinger fingerprint engine performance and reliability has been recognized by NIST as MINEX compliant.
VeriFinger SDK is offered for a competitive price and developers can select from these types of SDK:
VeriFinger Embedded SDK
VeriFinger Embedded is intended for mobile biometric systems developers and integrators. The technology assures fast fingerprint capture and fingerprint matching in 1-to-1 and 1-to-many modes with AFIS-level reliability. VeriFinger Embedded is available as a software development kit that allows development of stand-alone or Web-based solutions for smartphones, tablets and other devices that are running Android OS.
Free Fingerprint Verification SDK:
In 2008 Neurotechnology released Free Fingerprint Verification SDK. This freeware SDK is intended for adding fingerprint verification functionality into various applications. The SDK is most suitable for developing biometric logon applications, but it can be used also for any other application that do not require to store more than 10 fingerprints.
Free Fingerprint Verification SDK is available for downloading.
MegaMatcher Software Development Kit (SDK):
MegaMatcher is a multi-biometric technology, intended for large-scale AFIS or multi-biometric fingerprint, face, iris and palm print system integrators. The technology includes fingerprint, facial, iris, voice and palm print recognition engines that could be used either separately or together.
MegaMatcher 4.4 SDK includes server software for local multi-biometric systems, cluster software for large-scale multi-biometric products development, and a set of valuable task-specific components.
MegaMatcher On Card Software Development Kit (SDK):
MegaMatcher On Card is based on MegaMatcher multi-biometric AFIS technology and intended for systems that match fingerprints, irises and/or faces on smart card.
MegaMatcher On Card 3.1 SDK includes smart cards with pre-loaded fingerprint, iris and face matching engines, as well as PC-side fingerprint, iris and face template extraction components.
SDKs for mobile devices:
More products for developers: