Fujitsu develops 2,048-bit palm vein biometrics
August 6, 2013 - Fujitsu Laboratories
has announced that it has developed technology to extracting and
match 2,048-bit feature codes from biometric palm vein images.
According to the company, in contrast
to existing matching processes of comparing vein feature patterns,
the new method employs feature codes extracted from vein images that
represent the features of the images in binary format. This, in turn,
allows for simple comparison calculations and rapid authentication.
As multiple feature codes can be generated from a single piece of
biometric data, different codes can be used for different biometric
authentication services. As a result, even in the case of leaked
registered data, a new feature code can be generated and registered
to give users peace of mind and uninterrupted service.
The supplier hopes that the new
technology will expand the scope of use for palm vein authentication
technology, while also enabling a larger number of customers to
safely and securely take advantage of biometric authentication.
Technological Issues
Generating multiple biometric features
from a single piece of biometric data makes it possible to register
data that differs according to each service. Moreover, even if the
registered data were to be lost, it would be possible to change a
conversion method and generate a new biometric feature code, thereby
delivering an uninterrupted service with peace of mind. Technology
that can generate multiple feature codes from a single piece of
biometric data is known as "cancelable biometrics" or
"renewable biometrics".
With palm vein authentication, it is
possible to enable cancelable biometrics by transforming and storing
the original vein image or the extracted palm vein feature pattern
based on a conversion method. However, because comparisons require
pattern matching processing, as with previous vein authentication
technology, the process can be slow. Alternatively, the pattern
matching process can be avoided by extracting a feature code that
numerically represents the pattern feature of a vein image and
matching this feature code through a simple numerical computation.
This method reduces processing time but is also problematic in that
the feature code is easily impacted by the hand's position (the
incline of the hand e.g. tilted forward or to the side) and its shape
(e.g. if the hand stretched out or slightly relaxed) when capturing
the pattern.
