Exaggerate Self Quotient Image Model For Face Recognition Enlist Subspace Method

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 6, June 2011
Exaggerate Self Quotient Image Model for Face
Recognition Enlist Subspace Method
S.Muruganantham1, T.Jebarajan2
Assistant professor Principal
S.T.Hindu college Kings College of Engineering
Nagercoil, India -629003 Chennai, India - 600105
Email: smuru_1970@yahoo.co.in

Abstract - The attribute of reliance facial Up to now many prosperous algorithm
recognition technique is frequently impinge are intend for face recognition. Principle
on variation of illumination such as shadow component analysis ( PCA ) [1] , Independent
and illumination direction changes. In this component analysis (ICA ) [2] , Fisher’s linear
paper, to enrich the performance of self discriminant ( FLD ) [3] are three basic
quotient image model for the elimination of algorithm for subspace analysis in face
lighting effect for face recognition is recited recognition.
and dispelled in detail. A Histogram Besides the QI method, much other
equalization is embrace to enhance the technique has been proposed for face
contrast of samples. Then normalize the recognition under various illumination
samples as the result of enhanced SQI. To conditions in Holocene year. SQI perpetuate QI
apply enhanced SQI to face recognition to perform illumination subtraction without the
subspace analysis algorithms ( PCA, KPCA need for alignment and no shadow assumption
and ICA ) are tout to perform subspace as QI does.
analysis on normalized samples. To Recently some researchers paying close
prosecute experiments on CAS-PEAL face solitious to preprocessing of face images to
database. The face samples are preprocessed tune-up lighting in an image by initiate some
by enhance SQI to meliorate the models, like the quotient image QI [4,5,6]
performance of subspace analysis based methods, illumination cone based
algorithms. Experimental result canonical methods [7,8], total variation based methods
the preprocessing of samples could salutary (TV) [9,10]. Shashua et al. propound the
the robust to not only lighting but also facial quotient image which is an image ratio
expressions, masking and occlusion etc. in between a test image and a linear unification of
face recognition domain. three images illuminated by non coplanar
lights, authentic only on albedo information
Keywords: Face recognition, and therefore is illumination free.
Histogram equalization, Subspace analysis, QI method could improve the
illumination. performance, but it exigeny a large training set
and this is not practical in many predicament.
Wang et al. proposed a self quotient image
I. Introduction
model (SQI) [6].
The illumination problem has been SQI is proposed based on basic
believed to be one of most difficulties in face conception of QI. SQI implements the
recognition. The image variation from lighting normalization by dividing the low frequency
changes is more portentous than that from part of the prime image, since the lighting
divengent personal identities. effects are represented as the low frequency

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part. But the edge information is over illumination invariant properties as ostensive
smoothed by the smoother filter. To overcome below using Lambertian model but with
this problem by applying lighting invariant shadow. When shadow present the Lambertian
preprocessing methods. model equation can be epitomize a
In order to vanquish the drawback of
SQI, a new method enhanced self quotient I = max ( nT · S, 0) ------------- (3)
image is proposed and dispeled in detail. To
apply ESQI to face recognition, feature is Analysis
essensed using subspace method and the In a self quotient image, we regard
performance of PCA, KPCA and ICA are three regions with divergent shapes and
ascertained and compared. A large scale CAS- shadow conditions.
PEAL face database is used for check the Region 1:
validity of algorithms. Compared with SQI In the absence of shadow and presents
face sample preprocessed, the ESQI model small surface normal variation. In this case,
could improve the performance of face nT(u,v) ≈C1 , where C1 is a constant, then we
recognition algorithm. have

II. SQI frame work Q(u,v)= ≈ = ------- (4)

The Lambertian model can be
decomposition into two parts 1. The intrinsic In this case Q is quantisly illumination
part. 2. The extrinsic part. free and be contingent on the albedo of the face
Region 2:
I (x, y) = (x, y) n(x, y)T · S -------------- (1) In the absence of shadow but present
= F (x, y) · S large surface normal variation. In this case
nT(u,v) S is not a constant. The SQI is given
by
Where is albedo and n is the surface normal.
The main problem for accurate face
recognition is to separating the two factors and Q(u,v) = = -------- (5)
detach the extrinsic factors are a key to
attaining robust face recognition. In this case Q is contingent on surface normal,
albedo and illumination
III. Self quotient image Region 3

In shadow regions. In this case the gray
We define the self quotient image as an value of image is relatively low and does not
intrinsic property of face image. The self vary. Let us feigned that light is harmoniously
quotient image Q of image I is ipsofacto by scattered from all directions in shadow regions.
The summation of the dot product between n
Q= = ---------------- (2) and Si is constant in such region.

Where * is convolution operator, I is the NT(u,v)· Si(u,v)= nT(u,v)·Si(u,v)=C2
smoothed I and F is the smoothing kernel and
the division id point-wise as in the prime Where C2 is a constant and
quotient image. The self quotient image has

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Thus I(u,v) in shadow region can be
written as I(u,v) ≈C2 (u,v), then we have

Q(u,v) = = -----(6) ESQI
Fig-1
From Fig – 1 HE method does discard
In such region Q is illumination free most of impact of lighting from face images.
and also secluds the shadow effect. But HE has some obstacle. The face image
The advantage of SQI method is 1. preprocessed by HE method some important
Does not need alignment procedure. 2. No regions are blocked. Face feature like eyes nose
training images are exigency. 3. SQI is good and mouth are not persist in these blocked
for eradicate shadow. 4. Lightning source can regions. In our proposed method ESQI to
be any type. persist some important features after HE
method. Fig -1 also indicates some examples of
IV. Proposed method normalized face samples got by our model
(row marked with ESQI). Analogous with HE
In our proposed method we use a some consequential regions are not blocked.
weighted Gaussian filter, given by Analogous with ancestral HE method and SQI
method these advantages of our proposed
F = W G --------------------- (7) model make normalized face samples got by
our model more apt to face recognition on large
Where W is the weight and G is the Gaussian scale face database.
kernel. Given the training set X={x1,x2,……… xN}
In this application we adopt this filter where N is the number of samples.
with a multi scale version. Histogram In order to enhance contrast of every
equalization (HE) method has been sample xi wield histogram equalization, a flat
countenanced to be a cogent method to adjust histogram H1 with gray level K1 is procreated
lighting in images. Some Examples of where i = 1,2…………. , N.
preprocessed images by HE are illustrated in
fig -1( row marked with HE) H1 = * (n2/k1) ---------------- (8)

Accessory Distance Expression Lighting
A gray scale transformation T (· ) is
chosen to minimize
│C1 (T (K) ) ─ C0 ( K ) │
Where C0 (·) is the cumulative histogram of xi.
Original To procure the grayscale transformation
T(·) minimization of equation (8) may be
rewritten to minimize the error intervening the
optimal and actual cumulative histogram.
Histogram equalization(HE) The error is defined by

Err={CT - }+ ( ) --- (9)

SQI

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Where H0 is the actual histogram of xi, (595 males and 445 females) with varying
K0 is the gray level of H0, C0 is the actual pose, expression, accessory and lighting. In the
cumulative histogram, C1 is desired cumulative following experiment all the faces are rotated
histogram. Every sample xi is preprocessed to resized and cropped to 64 X64 with 256 gray
map its gray value from the actual K0 gray levels according to the coordinate of two eyes.
level to the desired K1 gray level and to enrich
its contrast as Four probe sets in the frontal subsets
expressions, lighting, accessory and distance
Xi = T (xi) -------------------------- (10) are chosen to the algorithm. To test the validity
using our proposed ESQI model in Face
For these enhanced face samples xi appertain to recognition.
the famous SQI method.
The following Table-1 illustrates the
Subspace analysis preciseness of recognition rate at rank -1 of
various approaches on the CAS-PEAL face
We appertain PCA, KPCA and ICA to
database respectively. It clearly show that our
perform subspace dialytic on these normalized
proposed model improve the performance of
face samples. Bartlett et al [2] has proposed
ICA2, KPCA and PCA against accessory,
and discussed two architecture about how to
distance and facial expression.
organize samples in the training set and they
also corroborate that the second architecture
L1 L2 Cos Md L1 L2 Cos Md
about how to organize samples could Accessory Distance

outperform the first one. So in this paper, the ICA2 48.93 41.79 52.74 40.75 78.18 72.56 85.46 71.96

second architecture about how to organize ESQI+ICA2 45.51 49.85 58.45 50.24 80.36 82.36 92.36 82.00

samples in the training set for ICA ( ICA2) [2] KPCA 40.20 32.60 20.18 46.00 86.11 63.45 30.15 74.64

is chosen. KPCA+ICA2 48.97 38.30 36.30 50.32 90.12 90.45 75.32 81.66

PCA 46.15 25.50 15.06 40.35 84.18 54.78 26.44 75.64

In order to codify features extracted by PCA+ICA2 51.55 35.72 32.91 50.81 91.22 81.05 76.05 84.37

Expression Lighting
subspace analysis methods (PCA, KPCA and ICA2 55.03 54.18 60.10 56.63 8.22 7.98 10.39 10.67

ICA2) the nearest neighbor classifier is adopted ESQI+ICA2 52.91 65.03 71.09 63.15 5.62 10.89 19.35 8.24

with four famous distances (L1, L2, cos and KPCA 71.55 65.45 37.33 56.61 8.11 3.00 0.5 11.98

mid) as affinity measurement for classifier in KPCA+ICA2 75.11 70.11 65.98 64.26 12.00 6.05 7.09 10.00

face recognition. PCA 68.58 62.50 30.48 53.17 6.10 2.16 0.57 10.98

PCA+ICA2 75.12 63.85 65.35 64.20 12.29 6.50 3.77 10.05

The four distances are
L1 = DL1(x, y) = ∑ i │xi – yi │. Table-1. The accuracy recognition
L2 =DL2(x, y) = (x, y)T (x, y). rates(%) at rank 1 of various approaches on
Cos = Dcos(x, y) = −xTy ║x║║y║. the CAS-PEAL face database.
Mid = Dmid(x, y) = (x─y)T ∑-1(x, y).
Fig -2 gives the accuracy recognition
rate of PCA, KPCA and ICA2 employing our
IV. Experimental result proposed ESQI model at rank 1 -50. According
to fig -1 ICA2 achieved the best performance
We investigate our algorithm on the
for accessory, distance and lighting test sets.
famous CAS-PEAL face database is chosen to
KPCA actualized the best performance for
verify and validate of various algorithms. It
expression testing set.
contains 99,594 images of 1040 individuals

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If the average preciseness rate is Fig-2 (a, b,c,d). The accuracy recognition rates
considered ICA2 could outperform the other of PCA, KPCA and ICA employing our
two algorithms advocated ICA2 employing our proposed ESQI model at rank 1-50 with the
similarity measurement which has achieved the
proposed ESQI model for real application.
max accuracy recognition rate in Table-1

VI. Conclusion
The new proposed model enhanced self
quotient image rub-on to face recognition
employing sub space analysis method. To
practice the experiments on the CAS-PEAL
face database the samples are preprocessed by
our proposed model could improve the
Fig-2a performance of some notorious subspace
analysis algorithm. And these result confirm
that the samples preprocessed by our model
could make PCA, KPCA and ICA2 not only
potent to light but also potent to facial
expressions, masking and occlusion etc. Thus
the new proposed model is apt to real
applications.

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Fig-2b
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