Fingerprint biometrics

Content of this page

• About fingerprint identification
• Fingerprint recognition technology
• Related Products

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.

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.

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.

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 2.1 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.