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.
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
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
Large-scale AFIS and multi-biometric identification
MegaMatcher technology is intended for
large-scale AFIS and multi-biometric systems developers. The
technology ensures high reliability and speed of biometric
identification even when using large databases.
MegaMatcher 4.2 is available as a
software development kit that allows development of large-scale
single- or multi-biometric fingerprint, iris, face, voice or palm
print identification products for Microsoft Windows, Linux and Mac OS
Biometric SDKs – subsets of MegaMatcher
More biometric products:
Robotics and computer vision:
- General object
recognition for robotics and computer vision.
- Tolerant to
appearance, object scale, rotation and pose.