comparative study of face recognition algorithm on … · 2017-07-22 · comparative study of face...

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





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.






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

from face profile image sequences, IEEE Transactions on

Systems, Man, and Cybernetics 36 (2) (2006) 433–449.

[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).






[4] S. Liao, W. Fan, C.S. Chung, D.-Y. Yeung, Facial

expression recognition using advanced local binary

patterns, tsallis entropies and global appearance features,

in: IEEE International Conference on Image Processing

(ICIP), 2006, pp. 665–668.

[5] C.S. Lee, A. Elgammal, Facial expression analysis using

nonlinear decomposable generative models, in: IEEE

International Workshop on Analysis and Modeling of

Faces and Gestures (AMFG), 2005.

[6] M. Valstar, M. Pantic, Fully automatic facial action unit

detection and temporal.

[7] Singh, R., Vatsa, M., Noore, A.(2009): “Effect of plastic

surgery on face recognition: A preliminary study”, In

Proceedings of the IEEE Computer Society Conference

on Computer Vision and Pattern Recognition Biometrics

Workshop, 72-77. analysis, in: IEEE Conference on

Computer Vision and Pattern Recognition Workshop,

2006, p. 149


comparative study of face recognition algorithm on … · 2017-07-22 · comparative study of face...

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