Facial recognition using multisensor images based on localized kernel eigen spaces
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16-03-2010, 07:30 AM


Facial recognition using multisensor images based on localized kernel eigen spaces.

A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then project and implimentationed into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy

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Gundimada S, Asari VK.

Symetix, Walla Walla, WA 99362, USA.
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Abstract—A feature selection technique along with an information
fusion procedure for improving the recognition accuracy
of a visual and thermal image-based facial recognition system
is presented in this paper. A novel modular kernel eigenspaces
approach is developed and implemented on the phase congruency
feature maps extracted from the visual and thermal images individually.
Smaller sub-regions from a predefined neighborhood
within the phase congruency images of the training samples are
merged to obtain a large set of features. These features are then
project and implimentationed into higher dimensional spaces using kernel methods.
The proposed localized nonlinear feature selection procedure
helps to overcome the bottlenecks of illumination variations,
partial occlusions, expression variations and variations due to
temperature changes that affect the visual and thermal face
recognition techniques. AR and Equinox databases are used for
experimentation and evaluation of the proposed technique. The
proposed feature selection procedure has greatly improved the
recognition accuracy for both the visual and thermal images when
compared to conventional techniques. Also, a decision level fusion
methodology is presented which along with the feature selection
procedure has outperformed various other face recognition techniques
in terms of recognition accuracy.
Index Terms—Feature extraction, image fusion, kernel methods,
phase congruency.
I. INTRODUCTION
MULTIBIOMETRICS refers to the use of a combination
of two or more sensor modalities in a single identification
system. The reason for combining different sensor modalities
is to improve the recognition accuracy. A multisensor biometric
system involving visual and thermal face images is presented
in this paper. Face recognition is one of the most important
applications of image analysis, its prime applications being
recognition of individuals for the purpose of security. It is one
of the most nonobtrusive biometric techniques.
Even though face recognition technology [1] has moved from
linear subspace methods [2]—Eigen and Fisher faces [3], [4]
to nonlinear methods such as kernel principal component analysis
(KPCA) and kernel Fischer discriminant analysis (KFDA)
[5]–[8], many of the problems are yet to be addressed. Also,
the nature of research studies had been more on visible imagery. However, the conclusion in [9]–[11] was
that, though the recognition performance of thermal imagery degraded,
the fusion of both visible and thermal modalities yielded
better overall performance.
Feature-based face recognition techniques [12], [13] have
demonstrated the capability of invariance to facial variations
caused by illumination and have achieved high accuracy rates.
To make the recognition process illumination invariant, phase
congruency feature maps are used instead of intensity values as
the input to the face recognition system. The feature selection
process presented in this paper is derived from the concept of
modular spaces [14]–[16]. Recognition techniques based on
local regions have achieved high accuracy rates. Though the
face images are affected due to variations such as nonuniform
illumination, expressions and partial occlusions, facial variations
are confined mostly to local regions [17]. Modularizing
the images would help to localize these variations, provided
the modules created are sufficiently small. But in this process,
a large amount of dependencies among various neighboring
pixels might be ignored. This can be countered by making the
modules larger, but this would result in an improper localization
of the facial variations. In order to deal with this problem, a
module creation strategy has been implemented in this paper
which considers additional pixel dependencies across various
sub-regions. This helps in providing additional information that
could help in improving the classification accuracy. Also, linear
subspace approaches such as PCA will not be able to capture
the relationship among more than two variables. They cannot
depict the variations caused by illuminations, expressions, etc.,
properly. In order to capture the relationships among more
than two pixels, the data is project and implimentationed into nonlinear higher
dimensional spaces using the kernel method. This enables to
capture the nonlinear relationships among the pixels within the
modules.

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15-10-2012, 10:31 AM

Facial Recognition using Multi Sensor Images Based on Localized Kernel Eigen Spaces


.pdf   Facial Recognition using Multi Sensor.PDF (Size: 638.42 KB / Downloads: 21)

Abstract:

A feature selection technique along with an
information fusion procedure for improving the recognition
accuracy of a visual and thermal image-based facial
recognition system is presented in this paper. A novel
modular kernel Eigen spaces approach is developed and
implemented on the phase congruency feature maps extracted
from the visual and thermal images individually. Smaller subregions
from a predefined neighborhood within the phase
congruency images of the training samples are merged to
obtain a large set of features. These features are then
project and implimentationed into higher dimensional spaces using kernel
methods. The proposed localized nonlinear feature selection
procedure helps to overcome the bottlenecks of illumination
variations, partial occlusions, expression variations and
variations due to temperature changes that affect the visual
and thermal face recognition techniques. AR and Equinox
databases are used for experimentation and evaluation of the
proposed technique. The proposed feature selection procedure
has greatly improved the recognition accuracy for both the
visual and thermal images when compared to conventional
techniques. Also, a decision level fusion methodology is
presented which along with the feature selection procedure
has outperformed various other face recognition techniques in
terms of recognition accuracy.

INTRODUCTION

Multi biometrics refers to the use of a combination of
two or more sensor modalities in a single identification
system. The reason for combining different sensor
modalities is to improve the recognition accuracy. A multi
sensor biometric system involving visual and thermal face
images is presented in this project and implimentation. Face recognition is one
of the most important applications of image analysis, its
prime applications being recognition of individuals for the
purpose of security. It is one of the most non-obtrusive
biometric techniques. Feature-based face recognition
techniques have demonstrated the capability of invariance
to facial variations caused by illumination and have
achieved high accuracy rates. To make the recognition
process illumination invariant, phase congruency feature
maps are used instead of intensity values as the input to the
face recognition system.

EXISTING SYSTEM

Even though face recognition technology has moved
from linear subspace methods Eigen and Fisher faces to
nonlinear methods such as kernel principal component
analysis (KPCA) is an extension of principal component
analysis (PCA) using techniques of kernel methods. Using
a kernel, the originally linear operations of PCA are done
in a reproducing kernel Hilbert space with a non-linear
mapping and kernel Fischer discriminant analysis (KFDA)
we carry out Fisher linear discriminant analysis in a high
dimensional feature space defined implicitly by a kernel.
The performance of KFDA depends on the choice of the
kernel. We consider the problem of finding the optimal
kernel, over a given convex set of kernels. We show that
this optimal kernel selection problem can be reformulated
as a tractable convex optimization problem which interiorpoint
methods can solve globally and efficiently. And
many of the problems are yet to be addressed. Also, the
nature of research studies had been more on visible
imagery with less attention on its thermal counterpart.

PROPOSED SYSTEM

A multi sensor biometric system involving visual and
thermal face images is presented in this paper. Face
recognition is one of the most important applications of
image analysis, its prime applications being recognition of
individuals for the purpose of security. It is one of the most
non-obtrusive biometric techniques. Feature-based face
recognition techniques have demonstrated the capability of
invariance to facial variations caused by illumination and
have achieved high accuracy rates. To make the
recognition process illumination invariant, phase
congruency feature maps are used instead of intensity
values as the input to the face recognition system. The
feature selection process presented in this paper is derived
from the concept of modular spaces. Recognition
techniques based on local regions have achieved high
accuracy rates. Though the face images are affected due to
variations such as non-uniform illumination, expressions
and partial occlusions, facial variations are confined mostly
to local regions. Modularizing the images would help to
localize these variations, provided the modules created are
sufficiently small. But in this process, a large amount of
dependencies among various neighboring pixels might be
ignored.

IMPLEMENTATION

Implementation is the stage of the project and implimentation where the
theoretical design is turned into a working system. At this
stage the main work load, the greatest upheaval and the
major impact on the existing system shifts to the user
department. If the implementation is not carefully planned
and controlled it can cause chaos and confusion.
Implementation all those activities that take place to
convert from old system to the new one. New system may
be totally new, replacing and existing manual or automated
system or it may be major modification to an existing
system. Proper implementation is essential to provide a
reliable system to the meet the organization requirements.
Successful implementation may not guarantee
improvement in the organization using the new system, but
improper installation will prevent it.
The process of putting the developed system into a actual
case is called system implementation. This includes all
those activities that take place to convert old system to the
new system. The system can be implemented only after
through testing done and if it found to be working
according to the specification. The system personnel check
the feasibility of the system. The most crucial stage is
achieving a new successful system and giving new
confidence on the new system for the user that it will work
efficiently and effectively. It involves planning
investigation of the current system and its constraints on
implementation, design of methods to achieve the
changeover. The more complex the system being
implemented, the more involved will be the system
analysis and the design effort required just for
implementation. Thus system implementation has three
main aspects. They are education and training, system
testing and changing.

CONCLUSION AND FUTURE SCOPE

This paper presented a face recognition technique using
visual and thermal face images. The feature selection
strategy is robust to the variations that occur in the face
images captured in both visual and thermal spectra. The
novel modular kernel eigen spaces approach has been able
to provide high recognition accuracy in images affected
due to partial occlusions, expressions and nonlinear
lighting variations. Experimental results presented show
significant improvement in the recognition accuracy of
thermal images. The fusion procedure presented in the
paper has outperformed the individual modalities as well as
other data fusion techniques in terms of recognition
accuracy. This also indicates that proper use of
complementary information from different sensor
modalities would be more beneficial for accurate face
recognition.
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