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Detection and Recognition of Human Faces Based on Hybrid Techniques

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In this paper, a novel face detection and recognition approaches based on learning and transformation techniques, is implemented. Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC). After detecting the input image is face or not the curvelet transform is applied on the face image as features extraction method to reduce the dimensionality that reduce the required computational power and memory size. Then the Nearest Mean Classifier (NMC) is adopted to recognize different faces.Experimental results demonstrate successful face detection and recognition over a wide range of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.
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IJAC: Volume 5, No. 2, July-December 2012, pp. 115-126 International Science Press: ISSN: 0974-6277
Detection and Recognition of Human Faces Based on Hybrid Techniques
Alaa Sulaiman Al-Waisy*
*Department of Computer Science, Alma’ref University College, Iraq,
E-mail: king_alaa87@yahoo.com
Abstract: In this paper, a novel face detection and recognition approaches based on learning and
transformation techniques, is implemented. Detecting faces across multiple views is more challenging than
in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel
machine based approach for learning such nonlinear mappings to provi de effec tive view-based
representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is
used to project data into the view-subspaces then computed as view-based features. Multi-view face detection
is performed by classifying each input image into face or non-face class, by using a two class Kernel Support
Vector Classifier (KSVC). After detecting the input image is face or not the curvelet transform is applied on
the face image as features extraction method to reduce the dimensionality that reduce the required
computational power and memory size. Then the Nearest Mean Classifier (NMC) is adopted to recognize
different faces.Experimental results demonstratesuccessful face detection and recognition over a wide range
of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in
images from several photo collections.
Keywords: Face Detection, Face Recognition, Kernel Principal Component Analysis, Kernel Support Vector
Machine.
1. INTRODUCTION
Biometric recognition systems based on face recognition have shown excellent performance in the area of
secured to buildings/airports/seaports, border checkpoints, law enforcement, surveillance systems and
so on. Face recognition problem is very challenging because of variations in different face images of the
same person due to changes in facial expressions, multi-poses, illumination conditions, rotation, age, and
presence of beard, moustache and etc. [1]. Therefore developing a computational model of face recognition
is quite difficult, because faces are very complex. In general, a face recognition system involves three
important stages: Face detection, Feature extraction and (identification and/or verification).
Face detection is the first stage of an automated face recognition system, since a face has to be located
in the overall image before it is recognized[2].As computers become faster and more affordable, many
applications that use face detection/ localization are becoming an integral part of daily life. For example,
face identification system, face tracking, video surveillance and security control system, and human
computer interface. Those applications often require detected and segmented human face which is ready
to be processed[3],[4]. However detecting a face under various environments is still challenging work.
Some factors make face detection difficult. One is the variety of colored lighting sources; another is that
facial features such as eyes may be partially or wholly occluded by a shadow generated by a bias lighting
direction; and others are race and different face poses with/without glasses. Finally because faces are not
rigid and have a high degree of variability in size, shape, color, and texture[5]. Therefore detection rate
and the number of false positives are important factors in evaluating face detection systems[6]. This paper
describes progress toward a system which can detect faces regardless of pose reliably and in real-time. In
the presented system a kernel machine learning based approach for extracting nonlinear features of face
images and using them for multi-view face detection. KPCA is applied on a set of view-labeled face
images to learn nonlinear view-subspaces. Nonlinear features are the projections of the data onto these
nonlinear view-subspaces. Face detection is performed by using KSVC as the classifying function, based
on the nonlinear features. One distinctive advantage this type of classifiers has over traditional neural
networks is that Support Vector Machines (SVM) achieve better generalization performance. While neural
networks such as Multiple Layer Perceptrons (MLPs) can produce low error rate on training data, there
Detection and Recognition of Human Faces Based on Hybrid Techniques
116
is no guarantee that this will translate into good performance on test data[3]. The literature on SVMs
includes many types of pattern recognition topics like face authentication, face recognition, object detection,
text classification, image classification and voice identification [7]. In the next stage, of the implemented
system one of the most important transformation methods is applied based on the curvelet transformation.
The curvelet transform is applied as features extraction method to reduce the dimensionality that reduce
the required computational power and memory size. Then the Nearest Mean Classifier (NMC) is adopted
to recognize different faces. The results show that the implemented approaches yields high detection and
recognition rates even under different conditions of lighting or when add some noise to the testing images.
The remainder of the paper is organized as follows: Section 2 introduces basic concepts of face detection
and recognition methods (KPCA, KSVC and Curvelet Transform). Section 3 introduces the literature survey
about the face detection and recognition systems. Section 4 the implementedface detection and recognition
system. Section 5 shows experimental results. Section 6 the conclusion.
2. FACE DETECTION AND RECOGNITION
To address the face detection and recognition problems, an efficient approach is presented in this paper.
In the first problem, the kernel methods generalize linear SVC and PCA to nonlinear ones is used.The
trick of kernel methods is to perform dot products in the feature space by using kernel functions in input
space so that the nonlinear mapping is performed implicitly in the input space. In the second problem,
one of the transformation methods is implemented as features extraction technique. The transformation
is a process that transforms an object from a given domain to another which can be used for its recognition.
A large class of image processing transformations is linear in nature an output image is formed from
linear combinations of pixels of an input image [8].
2.1 Kernel Principal Components Analysis (KPCA)
The KPCA is the nonlinear version of PCA that is constructed by using a specified kernel function. As a
simple description for the PCA. The PCA is used to lower the dimensional space of the feature to reduce
the time complexities [9]. Eigenvectors of the covariance matrix of the face images constitute the eigenfaces.
The dimensionality of the face feature space is reduced by selecting only the eigenvectors possessing
largest eigenvalues Once the new face space is constructed, when a test image arrives, it is projected onto
this face space to yield the feature vector the representation coefficients in the constructed face space. The
classifier decides for the identity of the individual. according to a similarity score between the test image's
feature vector and the PCA feature vectors of the individuals in the database[10]. Using PCA for eigenfaces
method, feature vectors identifying each image can be obtained as follows:
Given a set of examples in RN represented by column vectors, subtract them by the their mean vector
to obtain the centered examples xi RN (i = 1, …., m). The covariance matrix is
C=1
1m
T
j j
j
x x
m(1)
Linear PCA is an algorithm which diagonalizes the covariance matrix by performing a linear
transformation. By using a nonlinear mapping, the data set can be mapped into a higher dimensional
feature space H. The representation of features in this high dimensional feature space helps the classifier
to perform better. Fortunately, for certain feature spaces H there is a function for computing scalar products
in feature spaces. This is known as a kernel function. By using a kernel function, every linear algorithm
that uses scalar products can be implicitly executed in H without explicitly knowing the mapping ,
constructing a nonlinear version of a linear algorithm. The nonlinear version of PCA that is constructed
by using a kernel function is known as kernel principal component analysis KPCA.Let us now generalize
classic PCA to kernel PCA. Let : x RN X H be a mapping from the input space to a high dimensional
feature space [11]. The covariance matrix in H is
C
=1
1
( ) ( )
m
T
j j
j
x x
m(2)
Detection and Recognition of Human Faces Based on Hybrid Techniques
117
To do this, we have to find the eigenvalues 0 and eigenvectors satisfying
=
C
(3)
All solutions v with 0 must lie in the span of (x1), (x2), …, (xm). Hence Eq. 2 is equivalent to
( ( ). )
k
x v
=
( ( ) . )
k
x Cv
(4)
For k = 1,2, ….., m
Because all v for nonzero must lie in the span of the xks, there exist coefficients ai such that
v=
1
m
i
(5)
Defining the matrix K = [Ki; j]m × m, the eigenvalue problem can be converted into the following:
m=K(6)
for nonzero eigenvalues. Sort i in descending order and use the first M m principal components vi
as the basis vector in H (In fact, there are usually some zero eigenvalues, in which case M < m). The M
vectors spans a linear subspace, called KPCA subspace, in H. The projection of a point x onto the k-th
kernel principal component vk is calculated as:
(vk. (x)) =
,
, ,
1 1
( ( ). ( )) ( )
m m
i ik i k i
i i
x x K x x
(7)
2.2 Kernel Support Vector Machines (KSVMs)
Support vector machines (SVMs) are a popular method for binary classification. SVMs can be seen as an
extension of the perceptron, which tries to find a hyperplane that separates the data [12]. A more detailed
discussion of the theory and applications of SVMs can be found in [7]. Consider the problem of separating
the set of training vectors belonging to two classes, given a set of training data (xi, yi), ……, (xm, ym) where
xi RN is a feature vector and yi {–1, +1} its class label. If the two classes are linearly separable, there
exists a separating hyperplane (w, b) is given by the function:
f(x) =
1
( . )
m
i i i
i
sign a y x x b
(8)?
In real-life problems it is rarely the case that positive and negative samples arelinearly separable.
Non-linear support vector classifiers map input space RN to a high dimensional feature space H by x
(x) H such that the mapped data is linearly separable in the feature space [13]. In short, a hard margin
SVM solves the quadratic program (QP1) which is given as follows. Find the Lagrange multipliers
1
{ }N
i
that maximize the objective function:
Q( ) = 1 1 1
1
( , )
2
m m m
i i j i j i j
i i j
y y k x x
(9)
Subject to
1
m
i i
i
y
= 0, 0 i C(10)
Where C is a user-specified positive parameter. If 0 i C, the corresponding data points are called
support vectors. Having the Lagrange multipliers, the optimum weight vector w could be computed by:
Detection and Recognition of Human Faces Based on Hybrid Techniques
118
w=
1
( )
m
i i i
i
y x
(11)
By taking the samples with 0 < i < C, the bias could be calculated by
b=
1 1
( , )
No. SV i j
i i j i
i
x SV x SV
y K x x
y(12)
Where No. SV is the number of support vectors with, 0 < i < C. Next, a separating hyper plane is
computed in H. The decision function becomes
f(x) =
1
( , )
S
N
i i i
i
sign y K x x b
(13)
Where Ns is the number of support vectors and K( , ) is a kernel function. Several kernels are possible
including radial basis functions, polynomial and sigmoid kernels. The choice of the kernel and kernel
parameters (e.g. the degree of the polynomial kernel) has to bemade by the user, and the optimal choices
are problem dependent [14], [15].
2.3 Curvelet Transform
To overcome the innate limitations of traditional multi-scale representations such as wavelet a novel
transform has been developed by Candes and Donoho in 1999 known as curvelet transform. Curvelet
transform is a multiscale pyramid with many directions, positions at each length and fine scales [16]. The
motivation for the development of the new transform was to find a way to represent edges and other
singularities along curves in a way that was more efficient than existing methods, that is, less coefficients
are required to reconstruct an edge to a given degree of accuracy [17].
The curvelet transform, like the wavelet transform, is a multiscale transform, with frame elements
indexed by scale and location parameters. Unlike the wavelet transform, it has directional parameters,
and the curvelet pyramid contains elements with a very high degree of directional specificity. Also, the
curvelet transform is based on a certain anisotropic scaling principle which is quite different from the
isotropic scaling of wavelets. In wavelet's isotropic principle, the length and width of support frame is of
equal size whereas in curvelet transform the width and length are related by the relation width length2
that is known as parabolic or anisotropic scaling [16]. There are two generations of the curvelet transform.
The idea of The First Generation Discrete Curvelet Transform (DCTG1) is first to decompose the image
into a set of wavelet bands, and analyse each band by a local ridgelet transform. It results in a large
amount of redundancy. Moreover, this process is very time consuming, which makes it less feasible for
facial features analysis in a large database [18].
To overcome on the all the drawbacks in the (DCTG1) such as the parabolic scaling ratio width length2
is not completely true and time consuming. The Second Generation Curvelet Transform (DCTG2)
introduced in 2006 is not only simpler, but is faster and less redundant compared to its first generation
version [19]. Currently two implementations of fast (DCTG2) are available i.e. Unequally-Spaced Fast
Fourier Transform (USFFT) Based Curvelet and Frequency Wrapping Based Curvelet. The difference is
the choice of spatial grid used to translate curvelet at each scale and angle [16].
3. LITERATURE SURVEY
Automatic face detection and recognition problems have attracted many researchers and scientists and as
a consequence, several techniques have been developed to solve these problems. Amongst all these
numerous techniques very few are capable of solving these problems in unconstrained environment.
Generally, several researchers in the field of face detection and recognition developed different detection
and/or recognition algorithms. Some of these researches are summarized below:
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119
Min-Quan Jing and Ling-Hwei proposed a method to detect a face with different poses under various
environments. On the basis of skin color information, skin regions are first extracted from an input image.
Next, the shoulder part is cut out by using shape information and the head part is then identified as a face
candidate. For a face candidate, a set of geometric features is applied to determine if it is a profile face. If
not, then a set of eyelike rectangles extracted from the face candidate and the lighting distribution are
used to determine if the face candidate is a nonprofile face [20]. YongqiuTu, Faling Vi and et al proposed
a face detector has been designed using multi-classifier combination method. The proposed detector
composes of three classifiers: Skin color detector, AdaBoost detector based on haar-Iike features, and eye-
mouth detector, a semi-serial architecture is designed to combine the three detectors ,which set up the
division and cooperation system and draw on each other's merits to implement the quick and efficient
facial detection [21]. PaymanMoallem and BibiSomayeh proposed a fuzzy rule based system for pose,
size and position independent face detection in color image. Subtractive clustering method is also applied
to decide on the numbers of membership functions. In the proposed system, skin-color, lips position, face
shape information and ear texture properties are the key parameters fed to the fuzzy rule based classifier
to extract face candidate in an image.Furthermore, the applied threshold on the face candidates is optimized
by genetic algorithm [22].
NiuLiping, Li XinYuan and et al presented a hybrid approach based on Bayesian and wavelet transform
for face recognition is proposed. Firstly the system uses (PCA) to select the first 10 candidate images.
Then these candidate images and each testing image are decomposed into low frequency and high
frequency sub-band images by applying wavelet transform. Finally Bayesian recognition is parallel
processed using these sub-band images [23].
Mohammed Rziza , Mohamed El .Aroussi and et al in this work, an efficient local appearance feature
extraction method based the Curvelet Transform (CT) is proposed in order to further enhance the
performance of the well known Linear Discriminant Analysis (LDA)method when applied to face
recognition [24].
Dinesh Kumar, Shakti Kumar and et al presented a PCA-Memetic Algorithm (PCA-MA) approach
for feature selection. The ( PCA) has been extended by MAs where the former was used for feature
extraction/dimensionality reduction and the latter exploited for feature selection. Simulations were
performed over ORL and YaleB face databases using Euclidean norm as the classifier. The same approach
has also been applied to (LDA) and Kernel PCA approaches with the MA [25].
4. IMPLEMENTED SYSTEM DESIGN
The implemented face recognition system can be summarized in the five steps as shown in the Figure (1).
Figure 1: The Block Diagram of the Implemented Face Detection System
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120
4.1 Image Capture Step
Face recognition attracts researchers' attentions for a long time and has already achieved high level success.
Most of these researches assume a clear and size-enough facial image which is available. Unfortunately,
the facial image quality can not be guaranteed in long distance person identification. Therefore performance
of automatic face recognition is generally determined by the quality of the photographic images used. In
constructed face database the white background is used, provided there is sufficient distinction between
the face/hair area and the background. Only one person should be present in the photograph and no
other person or object was present in the background covered in the face image.
4.2 Preprocessing Step
Any face recognition algorithm relies on the preprocessing operations implemented before the application
of the actual recognition algorithm. Here it is proposed a simple image preprocessing chain that appears
to work well for a wide range of biometrics recognition, reduce the computational time, eliminating many
of the effects of changing illumination and the noise while still preserving most of the appearance details
needed for recognition.
4.2.1 Image Size Normalization
Size normalization is an important pre-processing technique in face detection and recognition. Although
various effective learning-based methods have been proposed. It is usually done to change the acquired
image size to a default image size. In this paper the default image size is 256 × 256, on which the proposed
face detection system operates.
4.2.2 Median Filtering
Median filtering follows this basic prescription. The median filter is normally used to reduce noise in an
image especially obtained from a camera, somewhat like the mean filter. However, it often does a better job
than the mean filter of preserving useful detail in the image. This class of filter belongs to the class of edge
preserving smoothing filters which are non-linear filters. This means that for two images A(x) and B(x):
median [ ( ) ( )] median [ ( )] median [ ( )]A x B x A x B x
These filters smooth the data while keeping the small and sharp details. The median is just the middle
value of all the values of the pixels in the neighborhood. Note that this is not the same as the average
(or mean); instead, the median has half the values in the neighborhood larger and half smaller. The median
is a stronger “central indicator” than the average. In particular, the median is hardly affected by a small
number of discrepant values among the pixels in the neighborhood. Consequently, median filtering is
very effective at removing various kinds of noise. Figure (2) illustrates an example of median filtering.
Figure 2: Median Filtering Operation
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121
4.3 Face Detection Step
In this stage a kernel machine based approach has been presented for learning view-based representations
for multi-view face detection.
4.3.1 Kernel Principle Component Analysis (KPCA)
This algorithm can be achieved based on ability of the KPCA. KPCA feature extraction effectively acts a
nonlinear mapping from the input space to an implicit high dimensional feature space. It is hoped that
the distribution of the mapped data in the implicit feature space has a simple distribution so that a simple
classifier (which need not to be a linear one) in the high dimensional space could work well. The steps to
compute the principal components can be summarized as:
Compute the matrix K, see Eq. (2). In this paper the polynomial kernel is used as a kernel function:
K(xi, xj) = (a(xi, xj) + b)n(15)
Where a = 0.001; b = 1; n = 3 and xk RN are taken from the face images by rearranging the pixel
value order as shown in Figure (3).
Figure 3: Pre-processing Step
To acquire the eigenfaces, the face image data are converted from matrices to vectors, where the
vector version of each face is a column in a matrix. For example, for a training set of 250 images
each have 256 × 256 pixels will be converted into a matrix of that is 65536 × 250. This matrix of
face vectors uses KPCA to compute the eigenfaces.
The resulting eigenfaces are then point multiplied with the training set images to filter out outlier
data and focus the training on the principal features of the face. The resulting images have their
intensities scaled.
The first M = 50 most significant principal components are used as the basic vectors.Which is
aiming to train the KSVC to differentiate between face and non-face patterns for face detection.
Compute projections of a test point onto the eigenvectors.
4.3.2 Kernel Support Vector Machine (KSVM)
SVMs implement complex decision rules by using a non-linear function to map training points to a
high dimensional feature space where the labelled points are separable.A separating hyperplane is founded
which maximizes the distance between itself and the nearest training points this distance is called the
margin. The hyperplane is, in fact, represented as a linear combination of the training points. The steps to
find an optimal separable hyperplane (decision function) can be summarized as:
After further refining the images using a principal components approach, The resulting
processed images are converted into input vectors xi and class values yiwhere i {1, ..., p, p + 1, ...,
q} with p and q being the number of user and imposter images, respectively, and x has M dimensions
where the dimension is the number of pixels in the image. For the user images, yj = +1 where
Detection and Recognition of Human Faces Based on Hybrid Techniques
122
j {1, ..., p}, and for the imposter images, yk = – 1 where k {p + 1, ..., q}.
The data are passed to the SVM training along with the kernel type and kernel parameters. In this
paper is Radial Basis Function (RBF) Kernel which can be calculated as follow:
K(xi, xj) = exp (– Sigma * ||xixj||^ 2) (16)
Where Sigma is the width which is specified by the user.
The decision function will then be tuned to find the optimal SVM parameters for data. After the
training function is complete a training model is returned which will be used for the classification.
In the testing phase the acquisition and processing steps of the tuning images are the same as that
required for the training data except the images in this set are used for classification not training.
By using the tuning data sets into the SVM classification function the effectiveness of the system
and its kernels can be tested. The effectiveness is gauged by overall detection rate and false positive
number. Based on the rate for all the test sets the kernel and kernel parameters are adjusted until
the kernel and parameter combination with the highest accuracy can be founded.
4.4 Feature Extraction Step
The feature extraction is an inevitable step in the classification of high-dimensional data. In this stage, the
feature extractor attempts to reduce the data dimensionality by extracting the discriminate facial features
while discarding the features are considered redundant for classification purposes.In the implemented
system, the Curvelet Via Wrapping is applied on the detected face image in the face detection step. Curvelet
transform based on wrapping of Fourier samples takes a 2-D image as input in the form of a Cartesian
array f [n1, n2] such that 0 n1, n2 < N and generates a number of curvelet coefficients indexed by a scale j,
an orientation l and two spatial location parameters (k1, k2) as output. The discrete curvelet transform can
be implemented based on the wrapping algorithm. In this algorithm, four steps are carried out:
The 2D-Img is first transformed into the frequency domain by forward FFT and obtain Fourier
samples
1 2
ˆ[ , ]f n n
.
for each scale j and angle l do. Divide FFT into collection of Digital Corona Tiles (Wedges) by
using two windowing functions ‘radial window’ and ‘angular window’.
Apply the wrapping algorithm to the wedge data.
Apply the inverse 2D FFT to the wrapped data to get curvelet coefficients Figure (4).
Figure 4: Curvelet Coefficients
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4.5 Classification Step
The classification step is implemented based on the Nearest Mean Classifier (NMC). In the NMC, the
Euclidean distance from each class mean (in this case) is computed for the decision of the class of the
training sample and the test data. In mathematical terms, the Euclidean Distance between the test sample
Ap and any one of the training sample Bp
q
D(q) =
2 2
( ) ( )
( ( ) ) ( ( ) )
q q
m n p p p p
q q
m n p p m n p p
A A B B
A A B B (17)
Where m and n is the dimensions of sample and
p
A
and
q
p
B
is the mean value of the testing and
training samples, respectively. After computing the distance to each class if the testing image as the same
as the training image then the D(q) is equal to one else the return value between (0, 1).
5. EXPERIMENTAL RESULTS
The experiments were done by constructing multi-view face database. This database currently contains
colored face images of 50 persons. Each person is photographed against a uniform white background
using a single camera and identical settings. For each person, we take 10 photographs. Each photograph
has a different combination of viewpoint such as (frontal 0°, right 45°, right 90°, left 45° and left 90°) and
facial expression such as (smiling, laughing, neutral and closed eyes). Figure (5) shows the image variations
for three persons.
Figure 5: Image Variation of Some Persons in the Constructed Multi-View Face Database
In this paper, we compare the performance of the implemented system by computing the face detection
rate and the number of false negative in five cases face detection. Considering the number of the training
and testing images to be 5. In the first case the face images are tested as they are captured. In the second
and third cases the illumination conditions of the training and testing images are changed respectively, as
shown in the Figure (6). In fourth and fifth cases some ratio of noise is added to the training and testing
imagesrespectively (Salt and Pepper type is used in these two cases). The face detection rate and the
number of false negative of the proposed system in these five cases are summarized in Table 1. The
training time and testing time are summarized in Table 2. Every algorithm used 255 images in the training
phase (face and non-face) and the testing time is also calculated for each image.
Detection and Recognition of Human Faces Based on Hybrid Techniques
124
Another experiment was done, the implemented face detection system was tested with theimplemented
curvelet technique together as a completed face recognition system. In this experiment, the image firstly
inputs to face detection system. If the image is detected as the face then it inputs to the curvelet technique
as the feature extraction algorithm. Then the NMC is adopted to recognize different faces. The testing
was done in the five cases as explained in the first experiment based on the same training. The experiment
results in all the five cases were explained in the Table (3).
Figure 6: Illumination Change (a) Ori ginal Image and (b) After Illumination Change
Table 1
The Face Detection Rate and the Number of False Negative
Detection Rate False Negative Number of Image The Case Number
First Case 255 14 94.4
Second Case 255 6 97.6
Case Third 255 15 94
Fourth Case 255 10 96
Fifth Case 255 17 93.2
Table 2
The T raining Time and Testing Time
Testing Time (s) Training Time (s) The Method
KPCA 184.522940 0.101256
KSVM 1.987141 0.00042
KPCA-KSVM 186.10081 0.101676
Table 3
The Face Recognition Rate
The Case Number Recognition Rate
First Case 89.4
Second Case 90
Case Third 89
Fourth Case 87
Fifth Case 86
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6. CONCLUSION
In this paper, a novel face detection and recognitionapproaches based on learning and transformation
techniques, is implemented. A kernel machine approach has been presented for learning view-based
representation for multi-view face detection. The main part of the this stage is the use of KPCA for extracting
nonlinear features for each view by learning the nonlinear view-subspace using kernel PCA. This is to
construct a mapping view from the input image space, in which the distribution of data points is highly
nonlinear and complex. In lower dimensional space the distribution becomes simpler, tighter and therefore
more predictable for better modeling of faces. The kernel learning approach leads to an architecture
composed of an array of KPCA feature extractors, one for each view. Multi-view face detection is performed
by classifying each input image into face or non-face class, by using a two class Kernel Support Vector
Classifier (KSVC). After detecting the input image is face or not the curvelet transform is applied on the
face imageas features extraction method to reduce the dimensionality that reduce the required
computational power and memory size. Then the Nearest Mean Classifier (NMC) is adopted to recognize
different faces. The experiments were done by constructing multi-view face databaseand the experimental
results demonstrate successful face detection and recognition approaches over a wide range of facial
variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images
from several photo collections.
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