Evolutionary
Reconfigurable Architecture
for Robust Face Recognition In Ja Jeon, Boung MO Choi, Phil1 Kyu Rhee Intelligent Media Lab., Department of Computer Science & Engineering Inha University, Yong-Hyun Dong, Incheon, Korea Biometric Engineering Research Center (juninja, chobm77)@ im.inha.ac.kr,
[email protected]
Abstract
method. The experiment performed using the EM shows very encouraging result, especially for changing
This
paper
architecture
proposes
a
with capability
called ERM(Evolutiona y
novel
illumination and noisy environments.
of evolution/adaptation, Reconfigurahle
and it is implemented partially Evolutionary
reconfigurahle Machine),
on FPGA
1 Introduction
chip.
module which has been implemented by
Advances in hardware technology
over last decade
feature space to achieve an optimal face recognition
enforce us a completely new concept of computer types the characteristics of which is quite different from
configuration of the ERM. Since a priori information of
previous
noise and system working
computation
parallel
genetic algorithm
available,
heuristic
evolves filter
intuitive
blocks and
environment decisions
are not or
time-
ones. Recently is performed
instead of software
the
complex
and
fast
by dedicated hardware
in digital
computer because
consuming recursive calculations are usually required.
hardware can operate in parallel, and the concept of an
of filter combination, associated parameters, and structure of
reconfigurable hardware and evolvable hardware has
feature space adaptively to unknown illumination
A large number of reconfigurable
The ERM can explore optimal configuration
and
been studied actively[ 1,3,4]. architectures have
noisy environments. Some of the commonly used filters
been investigated [S, 61.Reconfigurable architecture can
such as median filter,
be classified into gate-level and functional-level
homomorphic filter filter
histogram equalization filter,
and illumination
compensation
are designed and verified by implementing on
FPGA hardware. Parallel genetic algorithm evolves the and parameters of image enhancement
connection
ones.
Gate-level reconfigurable architecture has been studied in many research institutes, but at this time functional level
reconfigurable
intensively.
architecture
In addition, Xilinx
is
also
studied
decided to end the
filters as well as feature space of Gahor representation.
production of XC6200 FPGA which was the main
has been tested to the face recognition in
platform of gate-level reconfigurable architecture and
va ying environments of illumination and noise patterns.
gate-level reconfigurable architecture cannot be applied
The va ying
to a complex and large-scale system which is required
The EM
direction, composition.
illumination contrast,
environments include light hrigh tness,
The proposed
and spectral architecture for face
recognition adapts itself to varying illumination
and
noisy environments using the evolutiona y computing
to
solve
real
world
problems.
Functional
level
reconfigurable architecture covers large-scale function elements and can be applied to a complex and hugescale system [6]. In this paper, functional level reconfigurable approach
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with the capability of evolution is applied to enhance
ERM is depicted in Fig. 1. The ERM consists of the
the performance of face recognition especially in varying environment of illumination and noise. The
reconfigurable
proposed architecture,
the ERM
filter
module, reconfigurable
feature
space of Gabor representation, and evolution module.
consists of the
reconfigurable filter module, the reconfigurable feature space module, and the evolutionary
module.
2.1 Recontigurable Filter Module
In
the reconfigurable functional module, the basic evolvable unit, is the image enhancement filter.
Median filter
Evolution module evolves the interconnection structure
Median filter is useful to remove impulse noise. The
(sequent order) and the parameters of the filters in order
filtered gray levels are calculated by taking median of
to remove random noises in an image. Evolutionary
gray level of each pixel in mask set as described in
module explore the feature space of Gabor representation to find optimal feature representation. This
below equation.
enables to recognize the face in varying illumination
as shown in Figure 2, and genetic algorithms select
environments
optimal mask set.
module
adaptively.
designed by
(Hardware
Description
The recontigurable
Handel-C
instead of
Language), which
filter
In this paper, six types of median filter masks are used
HDL
is used
commonly. Handel-C makes it easier and faster to design hardware because of software-like design style. The ERM is an evolutionary reconfigurable architecture which can be implemented by programmable hardware. The ERM is based on evolving the circuit configuration instead of designing it in the ordinary way. That is, randomly evolutionary
generated configurations
guided by the
module are tested in a programmable
hardware device. The configurations
that make the
device output responses optimal to the desired response are combined to make better configurations
until an
optimal architecture is achieved. The ERM continues to
Figure
reconfigure
architecture
itself
in
order to
achieve
a better
1. The
proposed
evolutionary
reconfigurable
ERM.
performance. The chromosome of the evolutionary module specifies the function type of the evolution and the interconnection among evolution units. In Section 2, Evolutionary Reconfigurable Architecture is presented. Filter blocks that are base components of evolvable hardware. The overview of face recognition
Figure 2. Median filter mask set.
system including image enhancement filters and Gabor filter. In Section 3 describes the hardware
Histogram Equalization
Filter
implementation.
Section 4 describes the face recognition using the ERM. The experimental results
To improve contrast of image, histogram equalization is
are given in Section 5. In Section 6 the concluding
used. If the distribution of gray level was biased to one
remark is given.
direction or scaled value was not uniformly distributed, histogram equalization is a good solution for image
2 Evolutionary re
Reconfigurable
Architectu
enhancement. The result of histogram equalization is achieved by following three steps. 1) Count the number of occurrence for each gray scale
The outline of the proposed evolutionary architecture
levels and draw histogram.
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2) Find the normalized cumulative histogram as shown
the pixel value oforiginal image. In order to enhance
in equation (2).
the dark image, G(x,y) computed by equation (3) is
H,(i)
= hHf(i) n
added to the average of total original image pixel as
(4
described in below equation.
H, :normalized cumulative histogram
G(x, y) = max{l(x, u) - r’(x, y),O} + Ave
(4)
Hf :cumulative histogram of original image fm age Width Im age Height
g max:maximum contrast
c
n :number of pixels
where Ave =
3) Find the new contrast value by mapping normalized
I(x+w,
c
y+h)
w=o h=O Im age Width x Im age Height
The shadow part in an image comes to blur and stain
cumulative histogram to gray scale.
in the compensated image computed by equation (4). Homomorphic
So illumination
Filter
compensation is performed by a ratio of
original image to background illumination
modeling
When the image formation process is viewed as a product of image illumination and scene reflectance, it
function. The final compensated image is obtained by multiplying the ratio and weight value as shown in
is natural to remove the low frequency variations due to illumination by taking the log of the image before high
equation (5).
pass filtering and then the exp to display the result. This is exactly what homomorphic filtering does to enhance details in an image. That is, Homomorphic filter reduce brightness and emphasize contrast in a frequency domain in order to improve a reflectance effect and decrease a lighting effect. Figure 3 shows the flow diagram of homomorphic filter.
G(~,i-)=max~~xw,
2.2 Reconfigurable Representation
0~
Feature
(5)
Space of Gabor
Gabor wavelet efficiently extracts orientation selectivity, spatial frequency, and spatial localization.
It is a
simulation or approximation to the experimental filter response profiles in visual neurons. Figure 3. Homomorphic
used for image recognition
filtering.
Gabor Wavelet is
due to its biological
relevance and computational properties. Gabor wavelet is one of the successful models that simulate biologically motivated receptive fields. A
Illumination
Compensation Filter
Illumination
compensation filter is the high pass filter
using the local brightness, which means the average of difference between the brightness of central pixel point and the brightness of total window [3].
receptive function can be defined for different classes of visual neurons. The receptive fields of the neurons in the primary visual cortex of mammals are oriented and have characteristic frequencies. These could be modeled 2-D Gabor filter. Gabor filter is known to be efficient in reducing redundancy and noise in images
I,=
i=-12
j=-n
(3)
(2n + Q2
171. Gabor wavelet is biologically
motivated convolution
G(x, u) = maxV(x, u) - I’(-G YN
kernels in the shape of plane waves restricted by Gabor
I’(x, u) : background illumination modeling function
kernel. The Gabor wavelet has shown to be particularly fit to image decomposition and representation. The
1(x, u)
: pixel value of original image (x,y)
G(x, u) : pixel value of enhanced image (x,y)
When window
size is (2n+l)x(2n+l)
the brightness
value G(x,y) of new central point (x,y) is as shown in equation (3). When equation (3) is applied to an image,
convolution
coefficients
for
kernels
of
different
frequencies and orientations starting at a particular fiducial point is calculated.
The Gabor kernels for a
fiducial point are defined as follows:
compensated image become totally dark compared to
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F , the feature vector of sub-image arround
G is
defined as the concatenation of 40 complex coefficients where u and v denote the orientation and dilation of the Gabor kernels,
as follows:
@ =:(x, y) , 11j1denotes the norm
operator, and the wave vector Ti;jV,” is defined as follows: m,,,
= 6% ~0s 4A
where k, = Yi2
k, sin 4 >”
(7)
The feature vector is normalized to zero mean and unit variance, and restructured by reflecting an evolutionary factor e to adapt to the external environment. Let G~;)(;) denote the normalized vector constructed from
and ~0, = rrpl8.
they are generated from one mother wavelet by dilation
G’;j, (adapted by the factor e and normalized to PY zero mean and unit variance), the Gabor feature vector
and rotation using the wave vector @,,, . Each kernel
Fee) at a fiducial point
The family of Gabor kernels is similar each other since
G is then defined as follows:
is a product of a Gaussian envelope and a plane wave. The first term in the brackets in Eq(l) determines frequency part of the kernel and the second term
The feature vector thus includes all the Gabor transform
compensates for the DC value, which makes the kernels DC-free. The effect of the DC term vanishes when the
at the fiducial point as X , G,,,(i), P = 0,,,,,4, it derives
parameter CT has sufficiently
an optimal discriminating
high values, where
information
for a given
CTdetermines the ratio of the Gaussian window width
external environment using the evolutionary
to wavelength.
discussed in the next section.
Gabor wavelet frequencies,
is usually
v =:0,...,4 , 171. The
p = 0 ,..., 7 [12,
characteristics
of
spatial
used at five and
eight
different
orientations,,
kernels
show
locality
and orientation
desirable
selectivity, a suitable choice for face image feature extraction for classification. The Gabor wavelet transformation defined by the convolution
of an image is
of the subarea of image
using a family of Gabor kernels as defined by Eq.(S). Let ffi
module
be the gray value of an sub-image around
2.3
Evolutionary
Module
The role of evolutionary
module is to restructure a
Gabor feature vector of a face for achieving optimal performance of recognition system in varying environment. Evolutionary computing is inspired by biological
evolution
process
[ 81.
Evolutionary
computing initiates a population which is a set of members. Each member is described by a vector which is called a chromosome. The fitness of each member is
pixel 7;;j =Ix, Y, and the Gabor wavelet transform of the
evaluated, and only a portion of the population
sub-image is defined as follows:
selected as the population of next generation. It is said survival of the fittest with analogy with biological
is
evolution. In general, new generation will have higher fitness score than its previous generation. This process is repeated until a best member, i. e., a best face where
G = X,y
operator.
The
, and * denotes the convolution Gabor
transform
shows
desirable
characteristics of spatial locality, frequency and orientation selectivity similar to those of the Gabor kernels. Each G,,“(z)
consists of different
local,
recognition
architecture is reached, or a generation
exceeds a desired criterion value.
Chromosome and Genetic Operators
frequency and orientation characteristics at the fiducial
As shown in figure 6, each chromosome has total 295 bits in three parts. The first part describes the filter
point. x Those features are concatenated together to
switch and the filter selection. Sl is a field of the filter
generate a feature vector F for the fiducial point x.
switch. For example, when SEis 0011, Homomorphic filter
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and median filter are selected and illumination filter and histogram filter are not used. S2representsthe sequentorder of filters. Becausethe 24 combinations of four filters exist, 5 bits are required. In the second part, parameter values of homomorphic filter and median filter are described. The parameterof median filter determinesa type of median filter mask. And the third part is the feature selection mask in coefficients of Gabor filter. Feature points are six pixels around eyes and the number of coefficients of Gabor jet at eachpixel are 40. Totally, 240 coefficients exist.
As it searches the genospace, the GA makes its choices via genetic operators as a function of probability distribution driven by fitness function. The genetic operators used here are selection, mutation.
Figure 5. Chromosome
structure
crossover, and
of Gabor vectors for
fiducial points and fiducial points.
The Fitness of GA 19
258
4
9
16
0
S 1 : Filter switch(4bits)
The evolutionary
S2 : filter sequent order(5bits)
function to evaluate current population and choose offspring for the next generation. Evolution or Adaption
S3 : Homomorphic filter parameter(7bits) S4 : Median filter parameter(3bits)
point.
evolvable adaptation has achieved so far, and the class
The chromosome
represents the all possible combination points
and their
optimality classification
of
of fiducial
Gabor feature vectors, and the
the
chromosome
is
accuracy and generalization
The
system performance denotes the correctness that the
mapping
combination of vector element of each Gabor feature for each fiducial
fitness
performance and the class scattering criterion.
GAS are employed to search among the different combination of fiducial points and the different vector
needs a salient
is guided by the fitness function defined for the system
S5 : Feature selection mask(240bits) Figure 4. Structure of chromosome
module
defined
by
capability.
scattering indicates the expected fitness on future generations. The evolutionary
module
derives the successful classifier being balanced between recognition and generalization capabilities. The fitness function can be defined as follows: 77m = 4% m + A277gm where &)
(13)
is the term for the system correctness, i.e.,
The total Gabor feature vector for all fiducial points, v
successful recognition rate and II,(V) is the term for
is evolved from a larger vector set defined as follows:
class generalization.
A, and A,are positive parameters
that indicate the weight of each term, respectively. where
