04-10-2016, 11:00 AM
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Multibiometrics can provide higher
identification accuracy than single
biometrics, so it is more suitable for some
real-world personal identification
applications that need high-standard
security. Among various biometrics
technologies, palmprint identification has
received much attention because of its good
performance. Combining the left and right
palmprint images to perform
multibiometrics is easy to implement and
can obtain better results. However, previous
studies did not explore this issue in depth. In
this paper, we proposed a novel framework
to perform multibiometrics by
comprehensively combining the left and
right palmprint images. This framework
integrated three kinds of scores generated
from the left and right palmprint images to
perform matching score-level fusion. The
first two kinds of scores were, respectively, generated from the left and right palmprint
images and can be obtained by any palm
print identification method, whereas the
third kind of score was obtained using a
specialized algorithm proposed in this paper.
As the proposed algorithm carefully takes
the nature of the left and right palmprint
images into account, it can properly exploit
the similarity of the left and right palmprints
of the same subject. Moreover, the proposed
weighted fusion scheme allowed perfect
identification performance to be obtained in
comparison with previous palmprint
identification methods.
Palm print recognition inherently
implements many of the same matching
characteristics that have allowed fingerprint
recognition to be one of the most wellknown
and best publicized biometrics. Both
palm and finger biometrics are represented
by the information presented in a friction
ridge impression. This information combines ridge flow, ridge characteristics,
and ridge structure of the raised portion of
the epidermis. The data represented by these
friction ridge impressions allows a
determination that corresponding areas of
friction ridge impressions either originated
from the same source or could not have been
made by the same source. Because
fingerprints and palms have both uniqueness
and permanence, they have been used for
over a century as a trusted form of
identification. However, palm recognition
has been slower in becoming automated due
to some restraints in computing capabilities
and live-scan technologies.
Palm recognition technology exploits some
of these palm features. Friction ridges do not
always flow continuously throughout a
pattern and often result in specific
characteristics such as ending ridges or
dividing ridges and dots. A palm recognition
system is designed to interpret the flow of
the overall ridges to assign a classification
and then extract the minutiae detail — a
subset of the total amount of information
available, yet enough information to
effectively search a large repository of palm
prints. Minutiae are limited to the location,
direction, and orientation of the ridge
endings and bifurcations (splits) along a
ridge path.
The images in Figure present a
pictorial representation of the regions of the
palm, two types of minutiae, and examples
of other detailed characteristics used during
the automatic classification and minutiae
extraction processes.
Palmprint identification is an important
personal identification technology and it has
attracted much attention. The palmprint
contains not only principle curves and
wrinkles but also rich texture and miniscule
points, so the palmprint identification is able
to achieve a high accuracy because of
available rich information in palmprint.
Various palmprint identification methods,
such as coding based methods and principle
curve methods have been proposed in past
decades. In addition to these methods,
subspace based methods can also perform
well for palmprint identification. For
example, Eigen palm and Fisher palm are
two well-known subspace based palmprint
identification methods.
In recent years, 2D appearance based
methods such as 2D Principal Component
Analysis (2DPCA), 2D Linear Discriminant
Analysis (2DLDA), and 2D Locality
Preserving Projection (2DLPP) have also
been used for palmprint recognition.
Further, the Representation Based Classification (RBC) method also shows
good performance in palmprint
identification. Additionally, the Scale Invariant Feature Transform (SIFT) which
transforms image data into scale-invariant
coordinates, are successfully introduced for
the contactless palmprint identification.
Extensive experiments show that the
proposed framework can integrate most
conventional palmprint identification
methods for performing identification and
can achieve higher accuracy than
conventional methods. This work has the
following notable contributions. First, it for
the first time shows that the left and right
palmprint of the same subject are somewhat
correlated, and it demonstrates the feasibility
of exploiting the crossing matching score of
the left and right palmprint for improving
the accuracy of identity identification.
Second, it proposes an elaborated
framework to integrate the left palmprint,
right palmprint, and crossing matching of
the left and right palmprint for identity
identification. Third, it conducts extensive experiments on both touch-based and
contactless palmprint databases to verify the
proposed framework.
In biometrics there are two types of identity
matching: identification and verification.
Identification is a one-to-many comparison
of an individual‘s biometric sample against a
template database of previously gathered
samples.
Verification refers to a one-to-one
comparison between a previously acquired
template of an individual and a sample
which we want to authenticate. An
application providing verification support
would also require someother means for the
user to claim his identity (e.g. information
contained in a smart card, keyboard for user
input), while for identification purpose this
is not needed.
Palmprint recognition uses the person‗s
palm as a bio-metric for identifying or
verifying person‘s identity. Palmprint
patterns are a very reliable biometric and
require minimum cooperation from the user
for extraction.
Palmprint is distinctive, easily captured by
low resolution devices as well as contains
additional features such as principal lines,
wrinkles and ridges. Therefore it is suitable for everyone and it does not require any
personal information of the user. Palm
normally contains three flexion creases
(principal lines), secondary creases
(wrinkles) and ridges. The three major
flexions are genetically dependent; most of
other creases are not. Even identical twins
have different palmprints. These nongenetically
deterministic and complex
patterns are very useful in personal
identification. Palm is the inner surface of
the hand between the wrist and fingers. Palm
area contains large number of features such
as principle lines, wrinkles, minutiae, datum
point features and texture images. Most of
the system uses the low resolution image.