Dimensionality lessening methods convert the data in the
high-dimensional space to a space of fewer magnitude. The data transformation
may be linear, as in principal components analysis, or non-linear. Many
biometric spaces, such as facial biometrics, comprised of a large number of facial
appearance which causes difficulties in scholarship and recognition process.
One of the main linear techniques for dimensionality reduction is
a Principal Components Analysis (PCA), which performs a linear mapping of the
data to a lower dimensional space in such a way, that the individuality of the
data in that low-dimensional space is maximized. However, the resulting
dimensions might not be always efficient for biometric applications according
to ambiguous subspaces problem. In this section, a survey on dimensionality
reduction and methods of choosing proper feature subspaces has been provided.
The argument to the use subspace-clustering dimensionality reduction is made
based on wide-ranging survey.
To validate the network and dimensionality lessening method, we
have to explain to overall model of multi-biometric structure. A multi
biometric system uses multiple sensors for data acquisition. This allows
capturing multiple samples of a single biometric trait (called multi-sample
biometrics) and/or samples of multiple biometric traits (called multi source or
multi-modal biometrics). This also allows the system to enroll and validate a
user who does not acquire a specific biometric identifier.
One of the main problems faced by biometric community is the
biometric system dependability and performance. Thus, the first goal is to find
the principal mechanism of the distribution of biometric features, or the eigenvectors
of the covariance matrix of the set of biometric images. These eigenvectors can
be thought of as a set of features which together characterize the difference
between biometric samples. As the primary biometric samples, we select
fingerprints and face images, since they provide important unpredictability in
quality and a large quantity of multi-dimensional vectors.
The suggested methodology was successfully tested on fingerprint
matching problem. The minutia extraction method which consists of estimation of
orientation field, ridge detection, and minutia detection was applied, with
geometrical aspects of the new method and the Hopfield Neural Network which is
used for identification process.
We now provide the experimental results to
showcase advantages of chaotic neural network for fingerprint recognition in
both accuracy and circumvention (resistance to errors). The test database
contains both high and very low quality fingerprint images, thus if high level
of accuracy is confirmed on those samples, the system possesses both precision
and circumvention. The first task was to find the minutiae set based on the
algorithm given in previous section. A binarized version of the input image is
created next. The finding process is done based in the ridge with one pixel
width, so the in the next process the thinning filter will be performed on the
image.
No comments:
Post a Comment