International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 6, June 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Comparative Study of Face Recognition Algorithm
on Cohn-Kanade Database
Radhika Sharma1, Abhay Sharma
2
1Research Scholar, Amity University, Rajasthan. India
2Assistant Professor, Department of Computer Science, Amity University Rajasthan
Abstract: Distinctive machine learning routines are deliberately inspected on a number of databases. After having a lot research in
this field,it is examined that LBP features are viable and productive for facial expression recognition. We further developed Boosted-
LBP to concentrate the most discriminant LBP feature, and the best recognition performance is achieved by utilizing support Vector
Machine classifiers with Boosted-LBP features. Also, we examine LBP features for low-determination facial expression recognition,
which is a discriminating issue however occasional tended to in the existing work. We see in our investigations that LBP elements
perform steadily and vigorously more than a valuable scope of low resolutions of face pictures, and yield promising outcome in
compacted low-resolution video sequence caught in real world environment.
Keywords: Face recognition, dummy face, Cohn-kanade database and biometrics
1. Introduction
In the age of rising criminal activity, recognition of face is
enormously important in the context of Personal Computer
vision, psychology, reconnaissance, misrepresentation
location, design acknowledgment, neural system, substance
based feature handling, and so on. Face is a non nosy solid
biometrics for distinguishing proof and consequently
offenders dependably attempt to conceal their facial organs
by diverse counterfeit means, for example, plastic surgery,
mask and sham. The accessibility of a complete face
database is vital to test the execution of these face
acknowledgment calculations. In any case, while existing
freely accessible face databases contain face pictures with a
wide assortment of stances, light, motions and face
impediments yet there is no fake face database is accessible
openly area. The contribution to this research paper is: i)
Preparation of dummy face database of 110 subjects ii)
Comparison of some texture based, feature based and all
encompassing face acknowledgment algorithm on that
dummy face database, iii) Critical examination of these sorts
of algorithm on sham face database. More than decade face
recognition has turn out to be progressively essential toward
computer vision, pattern recognition, observation, fraud
detection, psychological research, neural network, substance
based video processing, and so forth. Fast advancement of
face recognition is because of blend of the variables, for
example, dynamic improvement of algorithm, accessibility
of vast facial database and system for assessing the
execution of recognition algorithm. Subsequently Facial
Recognition Technology (FRT) has raised as an alluring
answer for location numerous contemporary necessities for
ID and confirmation of character identity. This paper
highlights the potential and impediments of the technology,
noticing those assignments for which it appears to be
prepared for arrangement, those zones where execution
hindrances may be overcome by future mechanical
advancements and its worry with adequacy reaches out to
moral contemplations [1,2,7,8]. For the advancement of FRT
face image database is required. A few specialists have
grown such a variety of genuine face databases [10] with a
considerable measure of covariates. They have outlined and
tried numerous algorithms for acknowledgment and
recognizable proof of human confronts and exhibited the
execution of the algorithms yet the execution of face
acknowledgment algorithms on dummy and fake
countenances are definitely not reported in the writing. Since
face is non-nosy physiological biometrics [12] for the check
of personality claim along these lines in the period of
expanding wrongdoing, crooks dependably pay more
thoughtfulness regarding conceal or alter their facial organs
by utilizing such a large number of manufactured systems,
for example, plastic surgery, disguise, mask and dummy
faces.
2. Literature Survey
Lot of research has been done in the area of facial
expression detection and recognition in past ten years, some
of them is explained below:
1. PCA (Principle Component Analysis): PCA stands for
principle component analysis In high-dimensional
information (video and data), this system is intended to
model direct variety. Its objective is to locate a situated of
mutually orthogonal basis function that catch the variance of
maximum difference in the information and for which the
coefficients are pair-wise de-correlated [3]. For directly
inserted manifolds, PCA is ensured to find the
dimensionality of the complex and produces a conservative
representation. PCA was utilized to depict face pictures as
far as an arrangement of premise capacities, or "eigenfaces".
Eigenfaces was presented early [4] on as capable utilization
of primary parts examination (PCA) to take care of issues in
face acknowledgment and discovery. PCA is an
unsupervised strategy, so the system does not depend on
class data. In our usage of eigenfaces, we utilize the Nearest
neighbor (NN) way to deal with group our test vectors
utilizing the Euclidean distance [2].
2. MPCA: (Multi linear principle component analysis). One augmentation of PCA is that of applying PCA to
Paper ID: SUB155925 2653
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 6, June 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
tensors or multi linear array which brings about a technique
known as multi linear principle component analysis (MPCA)
[5]. Since a face image is actually a multi linear array,
implying that there are two measurements depicting the area
of every pixel in a face image, the thought is to focus a multi
linear projection for the picture, as opposed to framing an
one- dimensional (1 D) vector from the face image and
discovering a straight projection for the vector. It is
suspected that the multi linear projection will better catch
the relationship between neighborhood pixels that is
generally lost in shaping a 1D vector from the picture [2].
3. LDA (Linear Discriminant Analysis): Fisherfaces is the
immediate utilization of (Fisher) linear discriminant analysis
(LDA) to face recognition [6]. LDA looks for the projection
axes on which the information purposes of distinctive
classes are far from one another while requiring information
purposes of the same class to be near to one another. Not at
all like PCA which encodes data in an orthogonal straight
space, has LDA encoded separating data in a straightly
divisible space utilizing bases that are not so much
orthogonal. It is for the most part accepted that calculations
in light of LDA are better than those taking into account
PCA. In any case, other work, for example, [7] demonstrated
that, when the preparation information set is little, PCA can
overcome the problem of LDA, furthermore that PCA is less
sensitive to different training information sets.
4. ICA (Independent Component Analysis): At the point
when applying PCA to number of images of face, we are
discovering a set of basis vectors utilizing lower order statics
of the connections between the pixels. In particular, we
amplify the variance between pixels to partitioned linear
conditions between pixels. ICA is a speculation of PCA in
that it tries to distinguish higher order analytical relation
between pixels to frame a superior arrangement of basis
vectors. In [8], where the pixels are dealt with as random
variables and the face image as results. In a comparative
manner to PCA and LDA, once the new basis vectors are
discovered, the training and testing information are
anticipated into the subspace and a strategy, for example,
NN is utilized for characterization. The code for ICA was
given by the provider to use in face recognition research [8].
5. Neural Network: To model our method for perceiving
countenances is imitated to some degree by utilizing neural
system. This is proficient with the point of creating
identification frameworks that fuses counterfeit knowledge
for the sole purpose of concocting a framework that is
astute. The utilization of neural systems for face
acknowledgment has been indicated by [9] and [10]. In [11],
we can see the recommendation of a semi-directed learning
system that uses bolster vector machines for face
acknowledgment. There have been numerous endeavors in
which notwithstanding the normal procedures neural
systems were executed. Case in point in [12] a framework
was recommended that uses a mix of eigenfaces and neural
system. In [13], first The dimensionality of face picture is
decreased by the Principal segment investigation (PCA) and
later the acknowledgment is finished by the Back
Propagation Neural Network (BPNN).
6. LBP (Local Binary Pattern Method)
Figure 1: sample face from cohn-kanade data set
The first LBP administrator was presented by Ojala et al.
[2], what's more, was demonstrated an intense method for
composition portrayal. The administrator names the pixels
of a picture by thresholding a 3x3 area of every pixel with
the middle esteem and considering the results as a parallel
number (see Fig. 2 for a representation), and the 256-
canister histogram of the LBP names processed more than a
district is utilized as a composition descriptor.
Figure 2: The basic LBP operator
The inferred twofold numbers (called Local Double Patterns
or LBP codes) systematize neighborhood primitives
including distinctive sorts of bended edges, spots, level
regions, and so on (as demonstrated in Fig. 2), so each LBP
code can be viewed as a small scale texture [3].
Figure 3: Examples of texture primitives which can be
detected by LBP (white circles represent ones and black
circles zeros).
The constraint of the fundamental LBP administrator is its
little 3x3 area which cannot catch prevailing components
with vast scale structures. Thus the administrator later was
stretched out to utilize neighborhood of distinctive sizes [2].
Utilizing roundabout neighborhoods and bi-linearly
introducing the pixel qualities permit any range and number
of pixels in the area. See Fig. 4 for samples of the expanded
LBP administrator, where the documentation (P,R) means a
neighborhood of P similarly divided examining focuses on a
circle of sweep of R that shape a circularly symmetric
neighbor set.
Paper ID: SUB155925 2654
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 6, June 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Figure 4: Three examples of the extended LBP: the circular
(8, 1) neighborhood, the circular (12; 15) neighborhood, and
the circular (16, 2) neighborhood, respectively.
7. Boosting LBP For Facial Expression Recognition
The above investigations clearly show that the LBP feature
are successful for facial expression recognition, and
performed pretty much too or better than reported existing
methods however with a noteworthy low-processing
favorable position. In the above examination, face pictures
are just as separated into little sub-locales from which LBP
histograms are removed and linked into a solitary
component vector. Nonetheless, obviously the separated
LBP elements depend on the isolated sub-areas, so this LBP
highlight extraction plan experiences settled sub-locale size
and positions. By moving also, scaling a sub-window over
face pictures, numerous more sub-areas can be gotten,
bringing numerous more LBP histograms, which yield a
more finish portrayal of face pictures. To minimize a
substantial number of LBP histograms fundamentally
presented by moving and scaling a sub-window, boosting
learning [53] can be used to take in the best LBP histograms
that containing much discriminative data. In [54], Zhang et
al. exhibited a methodology for face acknowledgment by
boosting LBP-based classifiers, where the separation
between comparing LBP histograms of two face pictures is
utilized as a discriminative component, and AdaBoost was
used to take in a couple of most productive components. In
our past work [55], we displayed a contingent shared data
base boosting plan to choose the most discriminative LBP
histograms for facial expression acknowledgment. We
watched that AdaBoost performs better than the restrictive
shared data based boosting when utilizing a few many frail
classifiers. In this manner, in this segment, we take in the
most discriminative LBP histograms utilizing AdaBoost for
better facial representation.
AdaBoost function provides very much effective and simple
approach for non linear classification. Adaboost examine the
small number of weak classifier, whose performance is just
above the manual guessing and boost the output of those
classifier up to the maximum accuracy. According to the
updated development decision will be taken by the
algorithm.
Figure 5: The sub-regions (LBP histograms) selected by
Adaboost for each emotion. From left to right: Anger,
Disgust, Fear, Joy, Sadness, and Surprise
In every iteration a histogram is plotted for each sub reason,
actually adaboost is a function used to find the sub region,
which contain maximum discriminative information about
the face. Finally we combine the feature selection of
AdaBoost function and SVM classifier. In some part we also
train the SVM with boosted LBP.
Figure 6:.Distributions of the top 50 sub-regions (LBP
histograms) selected AdaBoost for each expression
The expressions we are covering in this project are shown in
the above figure.
3. Conclusion and Future Work
In this paper, we introduce an extensive observational
investigation of facial expression acknowledgment in light
of Local Binary Patterns highlights. Distinctive order
systems are analyzed on a few databases. The key issues of
this work can be condensed as takes after:
1) Inferring a viable facial representation from original face
pictures is a fundamental move for effective facial
expression recognition. We experimentally assess LBP
features to depict appearance changes of expression of
images. Experiments represent that LBP features are
viable and effective for facial expression
acknowledgment.
2) One challenges for facial expression recognition
perceives expression of face at low resolutions, as just
compressed low resolution feature (video) information is
accessible in real-world applications. We examine LBP
technique for feature extraction on low-resolution
pictures, and watch that LBP elements are robust and
stable over wide range of low resolutions face images.
3) We include AdaBoost to take in the most discriminative
LBP features from a big LBP feature pool. Best
recognition performance is achieved by utilizing SVM
with Boosted-LBP features. Be that as it may, this
algorithm has constraint on generalization to other data
sets.
References
[1] M. Pantic, I. Patras, Dynamics of facial expression:
recognition of facial actions and their temporal segments
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[2] B. Fasel, J. Luettin, Automatic facial expression analysis:
a survey, Pattern Recognition 36 (2003) 259–275.
[3] Y. Tian, T. Kanade, J. Cohn, Handbook of Face
Recognition, Springer, 2005 (Chapter 11. Facial
Expression Analysis).
Paper ID: SUB155925 2655
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
Volume 4 Issue 6, June 2015
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
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Paper ID: SUB155925 2656