Evolutionary Reconfigurable Architecture for Robust Face Recognition

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feature space to achieve an optimal face recognition configuration of .... compensated image become totally dark compared to the pixel ... DC-free. The effect of the DC term vanishes when the parameter CT has sufficiently high values, where.
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