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INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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A TWO PHASE ALGORITHM FOR FACE RECOGNITION IN FREQUENCY
DOMAIN
Ms Archana H. Sable,
Dr. Girish V. Chowdhary
School of Computational Sciences,
Swami Ramanand Teerth Marathwada University, Nanded(M.S.),India
ABSTRACT
Various changes in illumination, expression, viewpoint, and plane rotation present challenges
to face recognition. Low dimensional feature representation with enhanced discrimination power is
of paramount importance to face recognition system. In this paper, we propose a two-phase
algorithm for face recognition method in frequency domain using discrete cosine transform (DCT)
and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude
spectrums are used to represent the face image, i.e. the transformed image. Then a two-phase face
classification method is applied to the transformed images. The algorithm works in two phases: its
first phase uses the Euclidean distance formula to calculate the distance between a test sample and
each sample in the training sets, and then exploits the Euclidean distance of each training sample to
determine K nearest neighbors for the test sample. Its second phase represents the test sample as a
linear combination of the determined K nearest neighbors and uses the representation result to
perform classification. The experimental results using FERET, ORL(case-I), and ORL(case-II)
databases are also presented and compared the two-phase face recognition method based on space
domain of face images.
Keyword: Face Recognition, DCT, DFT.
1. INTRODUCTION
Face recognition technology has a variety of potential applications such as security access
control, personal identification to human-computer communication, intelligent human machine
interface, and so on. In the last few years, a number of face recognition and speech recognition
methods have been proposed using transform domain such as principal component analysis(PCA)
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
[1],[2],[3],[4], linear discriminant analysis(LDA) [5][6], locality preserving projection (LPP) [7], [8],
DCT[9], DFT[10] and discrete wavelet transforms(DWT) [11], [12].
A good many of DCT-based and DFT-based face recognition methods have been proposed.
In [13], Jiang et al. investigated that a certain number of DCT coefficients are removed, the
corresponding facial image description by the remaining DCT coefficients are robust to lighting
changes and scale variations. Such good properties would be very useful for applications of face
recognition, video object tracking, object segmentation and visual content processing. In general,
there are two kinds of face recognition methods based on DCT and DFT. One only uses part of DCT
coefficients or DFT amplitude spectrums for face recognition. For example, Sanderson et al.[14]
used the DCT coefficients of neighboring blocks to generate an efficient feature for face recognition.
Ghanbari [17] proposed an efficient feature extraction method based on DCT pyramid for face
recognition. And the other based on DCT and DFT face recognition methods is holistic and uses all
features of DCT or DFT to perform classification. For instance, Er. et al. [15] demonstrated an
efficient method for high-speed face recognition based on DCT and radial basis function (RBF)
neural networks. In [16], they presented a technique for recognition of frontal human faces on gray
scale images. In this technique, the distance between the DCT of the face under evaluation and all
the DCTs of the faces database are computed. Jadhao.et at[18] used Radon transform and Fourier
transform for face recognition on ORL database and achieved a good performance. Furthermore,
more face recognition algorithms focusing on selection of efficient features from DFT or DCT can
be found in [19], [20], [21], [22], [23]. Specifically, 3D face recognition techniques based on 3DDCT features and 3D frequency-domain representation can be found in [24], [25],[26]. Theoretically,
transform-based approaches have a number of practical advantages. First, they have been proven to
be very efficient with face images of different features and illumination. Second, they have been
proven to be good with face images of different scales, poses and rotated images. Third, they may be
more efficient than in the pixel domain.
Recently, a method that addresses classification problems from a novel viewpoint has been
proposed. This method uses a two-phase test sample representation method to perform face
recognition [27]. In this method, the first phase represents a test sample as a linear combination of all
the training samples, and exploits the representation ability of each training sample to determine the
K nearest neighbors for the test sample. The second phase represents the test sample as a linear
combination of all the K nearest neighbors and uses the representation result to perform
classification. We note that the above method is a successful 2 norms based sparse representation
method. Though it obtains a very high accuracy, it is computationally more efficient than the naïve 1
norm-based sparse representation method proposed in[28]. Moreover, the 2 norms based sparse
representation methods not only can perform well in face recognition [29], [30], [31], [32], but also
achieve a good performance in palm print recognition [33] and bimodal biometrics [34].
Furthermore, the 2 norms based sparse representation methods have been extended into the feature
space [35].
The method proposed in this paper is holistic and uses all the DCT coefficients or the DFT
amplitude spectrums to generate the transformed images. Firstly, we apply the Euclidean distance
formula to measure the distance between a test sample and each sample in the training sets, and then
exploit the Euclidean distance of each training sample to determine K nearest neighbors for the test
sample. Then, we represent the test sample as a linear combination of the determined K nearest
neighbors and use the representation result to perform classification. In addition, we use various
numbers of DCT coefficients and DFT amplitude spectrums to test the effect on performance of our
algorithms. The experimental results demonstrate that our proposed algorithms are very competitive.
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ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
This paper is organized as follows. Section 2 describes the structure of the proposed method
for face recognition. Experimental results are presented and discussed in Section 3. Finally,
conclusions are drawn in Section 4.
2. THE PROPOSED ALGORITHM
The proposed method is divided into four stages: feature selection, the first phase of test
sample representation, the second phase of test sample representation and classification.
In feature selection stage, all the samples are converted into feature matrices by implying
DCT or DFT. Then, we extract the absolute values of DCT coefficients or DFT amplitude spectrums.
Furthermore, we convert each feature matrix into a matrix with the same size as the matrices of the
original images, which formed transformed images.
a
b
Fig.1. Result of DCT on a sample from the ORL database
(a) original image, (b) DCT image
Fig.2. Result of DFT on a sample from the ORL database
(a) original image, (b) DFT image.
Figs.1 and 2 show the application of the DCT and DFT on one of the face images obtained
from the ORL database. Fig.1.a and Fig.2.a display the original image. Fig.1.b and Fig.2.b display
the result of applying the DCT and DFT on the original image, respectively.
In the following sections, we assume that there are L classes and n training samples x1,…, xn.
If a sample is from the j th class j =1,2,…,L, we take j as the class label of the training sample. In the
first phase of the test sample representation, first of all, we use the Euclidean distance formula to
measure the distance between a test sample and all the training samples. The Euclidean formula for
distance in d dimensions is:
d
D(a.b )= ∑
n =1
1/ 2
2
(an −bn )
(1)
Where, a andb are the column vectors of samples.
Secondly, the Euclidean distance between a test sample y and the i th training sample will be
D( y, xi)(i=1,…, n) . A smaller D( y, xi) means that the i th training sample is more similar to the test
sample than other training samples, and which has a great contribution in representing the test
sample. Furthermore, we exploit D( y, xi)(i=1,…, n) to identify the K training samples that have the
K minimal distances, and denote them by x1…,xk . The test sample can be represented by x1…,xk. And
these samples are considered as the K nearest neighbors of the test sample. In addition, let
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4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
C ={ c1…,cd}, a set of some numbers, stand for the set of class labels of the K nearest neighbors. C
are one subset of set={ 1,…,L}In the second phase of the test sample representation, firstly, we
represent the test sample as a linear combination of the determined K nearest neighbors.
Accordingly, we assume that the following equation is approximately satisfied:
~
~
y = b1 x1 + ... + b K x K
(2)
~
Where xi (i=1,..,K) are the identified K nearest neighbors, and bi (i=1,…,K) are the coefficients.
Equation (2) shows that the K nearest neighbors of test sample makes their own contribution to
representing the test sample, which are the best representing of the test sample. If the dimensions of
~
xi (i=1,..,K) are d , the time complexity of Equation (2) will be O(d3 ) . Secondly, according to the
~
~
neighbors might be from different classes, all the neighbors from the r th ( r єc ) class are x s ,..., xt ,
then the sum of the contributions to represent the test sample of the r th class will be
~
~
g r = bs x s + ... + bt xt
(3)
In classification stage, first, we convert a test image into a transformed image ty by using the
features selection method, and then we calculate the deviation of gr from ty by using
D r = || t y − g r || 2 , r ∈ c
(4)
A smaller deviation Dr means a greater contribution to representing the test sample. Thus, we
classify ty into the class that produces the smallest deviation.
As a result, we use the Euclidean distance formula instead of the linear combination to
determine the K nearest neighbors of the test sample in the first stage. Thus, our proposed algorithms
have a lower time complexity than TPTSR.
3. EXPERIMENTAL RESULTS
In order to test the efficiency of our algorithms presented above, we perform a series of
experiments using the ORL [36] and the FERET [37], [38] face databases. The ORL face database
contains 400 images of 40 persons (10 images per person). The resolution of ORL images is 46×56.
The FERET face database contains 1400 images of 200 persons (7 images per person). The
resolution of FERET images is 40×40. Both of these databases, the images that belong to the same
person usually present variations in expression and illumination. Sample images from the two
databases are displayed in Figs 3 and 4.
Fig.3. Some Face Images of a Person from the ORL Database
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5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Fig.4 Some Face Images of a Person from the FERET Database
If s samples of the n samples per class are used for training and the remaining samples are
used for testing, there are
C ns =
n ( n − 1)...( n − s + 1)
s ( s − 1)... 1
possible combinations. As a result, there are Cns training sets and Cns test sets. For the ORL database,
we test different algorithms in two cases. In the first case, we use five images of each person as the
training samples and there are 252 training and test sample sets. In the second case, we use six
images of each person as the training samples and there are 210 training and test sample sets. For the
FERET database, we use four images of each person as the training samples and take the remaining
images as test samples. As a result, we test different methods by using 35 training and test sample
sets from the FERET database. When implementing our method, we solve B by using
~ ~
~
B = X T X + γI ) −1 X T Y . In order to eliminate the influence of the negative values, we use the absolute
values of DCT coefficients or DFT amplitude spectrums. Beyond that, γ is set to 0.001.
3.1 Performance comparison
This section compares our proposed algorithms performances, including the two-phase test
sample representation method based on DCT (TPTSR-DCT), the two-phase test sample
representation method based on DFT (TPTSR-DFT), two-phase face recognition method based on
DCT (TPFR-DCT), two-phase face recognition method based on DFT (TPFR-DFT), with four
existing algorithms, namely, TPTSR[27], SRC[28], PCA, LDA. The algorithms of TPTSR-DCT and
TPTSR-DFT use the linear combination of training samples to determine K neighbors. The
algorithms of TPFR-DCT and TPFR-DFT use the Euclidean distance between the test sample and
the training samples to determine the K neighbors.
Fig.5 Means of the rates of the classification errors (%) of our algorithms (TPTSR-DCT,TPFR-DCT,
TPTSR-DFT, TPFR-DFT), TPTSR on the ORL face database (the first case)
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6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Fig.6 Means of the rates of the classification errors of our algorithms (TPTSR-DCT,TPFR-DCT,
TPTSR-DFT, TPFR-DFT), TPTSR on the ORL face database (the second case)
Fig.7 Means of rates of the classification errors of our algorithms (TPTSR-DCT,TPFR-DCT,
TPTSR-DFT, TPFR-DFT), TPTSR on the FERET face database
Figs.5, 6 and 7 shows the means of rates of the classification errors (%) of our algorithms and
TPTSR for the ORL database (the first case), the ORL database (the second case) and FERET
database, respectively. Figs.5 and 6 clearly show that our proposed algorithms are always able to
obtain a much lower means of rates of the classification errors than TPTSR. Fig.7 shows the means
of rates of the classification errors of our proposed algorithms are lower than TPTSR in most cases.
But, the performances of TPTSR-DFT and TPFR-DFT are inferior to TPTSR when the number of K
is smaller certain values on FERET face database. In most cases, the performances of TPTSR-DCT
are better than TPFR-DCT on the ORL and FERET face databases. As for TPFR-DFT and TPTSRDFT, the performances of TPFR-DFT are better than TPTSR-DFT in most cases on ORL and
FERET face databases. Most important of all, those figures also show that our proposed algorithms
can achieve a better performance than TPTSR when they use a suitable K nearest neighbors training
samples to represent the test sample. Moreover, it can be observed than there is an increase in the
means of the rates of the classification errors as the numbers of K increase. But if we use a suitable K
nearest neighbors training samples to represent the test sample, the means of the rates of the
classification errors of our algorithms can achieve below 2% and 25% for ORL and FERET face
databases, respectively.
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ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Table.1. Mean of the rates of the classification errors (%) of Global version of our algorithms
(TPTSR-DCT, TPFR-DCT, TPTSR-DFT, TPFR-DFT), TPTSR, SRC, PCA, LDA
TPTSR
DCT
TPFR
DCT
TPTSR
DFT
TPFR
DFT
TPTSR
LDA
SRC
PCA
LDA
ORL-Case I
3.7
3.7
2.82
2.82
6.8
5.18
10.2
4.8
ORL-caseII
2.8
2.8
2.11
2.11
5.6
3.3
8.2
3.7
FERET
2.5
2.5
3.23
3.23
4.21
2.59
38.3
3.63
Table.1 shows the means of the rates of the classification errors of the global version of our
proposed algorithms, the global version of TPTSR, SRC, PCA and LDA in the ORL (the first case
and the second case) and FERET databases. The global version is the implementation of our
proposed algorithms in the case where K is equal of the total number of training samples. That is, in
the first stage of our algorithms, the test sample is represented by all the training samples. The global
version of K for the ORL and FERET face databases is 200 and 800, respectively. In
PCA algorithm, the rest of the feature vectors contain information account for 90% of total amount
of information. For the LDA algorithm, the transform axes of feature extraction on ORL database
and FERET database are 39 and 199, respectively.
Table.1demonstrates that the performances of our proposed algorithms are better than the
TPTSR, SRC, PCA and LDA. Moreover, TPTSR-DCT and TPFR-DCT achieve the best results on
the FERET database. The performances of TPTSR-DFT and TPFR-DFT are the best on the ORL
database (the second case). The reasons may be that the DCT coefficients and DFT amplitude
spectrums are more robust to lighting changes and scale variations than pixel gray values, which can
improve the face recognition performances. In addition, the test sample and its nearest neighbors
usually belong to the same class, so our algorithms provide a very reasonable way to determine the
class labels. Most important of all, our algorithms are an extended of TPTSR, which is a supervised
sparse representation method. Therefore, our algorithms have all the advantages of the SRC
algorithms, and achieve a better performance than the TPTSR, SRC, PCA and LDA.
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