Facial Expression Identification by Using Principle

ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
Facial Expression Identification by Using
Principle Component Analysis
Naiknavare Kishor1, Bhandwalkar Bhim2, Bothe Rushikesh3, Kumbhar Satish L
Department Of Computer Engineering, SBPCOE, Indapur (Pune University), India
[email protected], [email protected], [email protected]
.
ABSTRACT— Facial recognition technique makes it possible to use the facial images of a person
to authenticate him into a secure system, for criminal identification, for passport verification. Human
face is a complex multidimensional structure and needs good computing techniques for recognition.
Facial expression identification by using Principle Component Analysis (PCA) Mechanism. The face
is the main part of attention and plays an important role in identification. PCA is used for reducing
the number of variables in face recognition identification. In PCA faces are represented as a linear
combination of Eigen faces. In this paper the multiple expression images are taken for feature
extraction and compare it with the registered database image. PCA can identify the different
expression such as Happy, Anger, Sad, Disgust, Neutral, fear etc. Training process that read all the
faces where training database is stored and testing process that reads all the faces of the person
where the test folder.
Keywords: Face Recognition, Principle Component Analysis (PCA), Face database, Eigen face.
1. INTRODUCTION
Facial recognition technique makes it possible to use the facial images of person to
authenticate him into a secure system, for criminal identification, for passport verification
etc. A facial recognition system is a computer application or device that can identify
individuals based on their unique facial characteristics. Unlike many other identification
methods e.g. Fingerprints, voiceprint, signature, they do not need to make direct contact with
an individual in order to identify their identity.
Principle Component Analysis (PCA) is a method of classical feature extraction and
the data representation technique which is widely used in the pattern recognition. The
purpose of the PCA is reducing the large dimensionality data space into the smaller
dimensionality feature space need to describe the data economically [1]. Face is the
multidimensional structure and needs good computing techniques for recognition. Face
recognition is an integral part of the biometrics [2]. The biometrics basic that traits of any
human images that match to the existing database image and display the result according to
their database identification. This facial expression identification can be implemented by
using the PCA, because PCA is dimensionally reduced in data.
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ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
Facial expressions provide an important behavioral measure for the study of different
types of the emotions. Automatic facial recognition systems now have a potential to be useful
in several day-to-day application environments like in identifying suspicious persons in
airports, railway stations and other places with higher threat of terrorism attacks [3].
Fig.1. Examples of seven principal facial expressions [4]: smile, disgust, anger, surprise,
Fear, neutral, and sadness (from left to right).
Automatic classification of facial expressions is done with the help of the given input
image which is taken by camera and this input mage compared with the database image,
which is taken at the time of the registration. The psychologists have indicated that as least
six emotions are universally associated with distinct facial expressions, including smile,
sadness, surprise, fear, anger, and disgust.
2. LITERATURE RERIEVE
This paper introduced a Facial Expression Identification (FEI) of different expressions
of the person. In which the system identify the different expression of the person those
database are stored for identification. Face detection could be categorized into four group
feature invariant, knowledge-based method, approaches template matching methods, and
appearance based methods [8]. In this paper a new technique coined 2D principle component
analysis is developed for image reorientation [9].
NAME
METHOD
PERFORMANCE
Low-Dimensional Procedure for
Principle Component
Recognition rate is low
Characterization for Human Face.
Analysis
Recognizing Face with PCA and ICA
Independent Component
Recognition rate is
Analysis
improved compared to
PCA and FLD
Multi-linear Image Analysis for
Multi-linear Image
Recognition rate higher
Facial Recognition
Analysis
than PCA.
Table 1: Comparison table on literature survey [10].
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ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
3. PCA ALGORITHM
The Principle Component Analysis (PCA) is to find the vectors which best account
for distribution of face images within the entire images space [1]. PCA is a technique used
to lower the dimensionality of a feature space that takes a set of data points and constructs a
lower dimensional linear subspace that best describes the variation of these data point from
their mean [3]. PCA has been called one of the most valuable results from applied linear
algebra. PCA is used abundantly in all forms of analysis from neuroscience to computer
graphics [6]. In PCA Faces are represented as a linear combination of weighted eigenvectors
called as Eigen faces. These eigenvector are obtained from covariance matrix of a training
image set called as a basic function [5].
4. FACE RECOGNITION SYSTEM
The face recognition system consists of three main steps in the face Acquisition,
features Extracting and last is Face recognition. In this system the following fig. 1 indicates
the face recognition which is represent the input image as a source of the data for recognition
different facial expression. In which first need to take an input to recognize the result. Input
image is supply for the further process for acquisition. Above mentioned three steps are
followed sequentially.
Face
Acquisition
Feature
Extraction
Face
Recognition
Fig.1. Face Recognition System
Face acquisition and processing is the first step in the face recognition system. In
which face images is collected from different sources. In this system source is allowed for is
camera, we can assign different source as possible.
The collected face images should have the pose, illumination and expression etc
variation in order to check the performance of the face recognition system under this
condition [5]. Face recognition has received substantial attention from research in
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ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
biometrics, pattern recognition, and field and computer vision communities. The face
recognition system can extract the feature of face and compare this with the existing database
[7].
Feature
Extraction
Preprocessing
Classification
Input Image
Preprocessing
Feature
Extraction
Knowledge
Database
Happy
Sad
Surprise
Anger
Disgust
Fear
Neutral
Fig.2. Block Diagram of proposed system
The Facial Expression Identification (FEI) system of is given in fig.2. The Input image forms
the first state for the face recognition system. Different facial expression images passed as
input. Input image sample are considered of non-uniform illumination effects, variable facial
expression and face image. In second the operation will be done in this manner the face
image passed is transformed to operational compatible format, where the face image is
resized to uniform dimensional. In feature extraction process the PCA algorithm can run for
the computation of face recognition. These features are passed to the classifier unit for the
classification of given query with the different result such as Happy,
Sad, Surprise, Anger, Disgust, Fear and Neutral. For the implementation of proposed recognition
architecture the database sample are trained for the knowledge creation for classification.
5. EXIXTING SYSTEM
There are some existing system that are mentioned in this paper such as feature based,
Biometrics, Fisher linear Discriminate, Independent Component Analysis, 2Diamentional
Principle Component Analysis etc.
a) Feature based
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ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
b)
c)
d)
e)
Invariant features of face are used for detecting texture, skin color. One problem with
this feature-based algorithm is that the image feature can be severally corrupted due
to illumination, noise and occlusion.
Biometrics
Biometrics is used in the process of authentication of a person by verification or
identifying that a user requesting a network resource is who, he, she, or it claims to
be, and vice versa. It uses the property that a human trait associated with a person
itself like structure of face details etc. By comparing the exiting data with the
incoming data we can verify the identity of particular person.
Fisher linear Discriminate
In facial expression and illumination Fisher’s Linear Discriminate is more suitable.
It reduces the scattering of projected sample since it is class specification method
[11]. Error rate is reduced when compared to PCA.
Independent Component Analysis
PCA and linear discrimination analysis generate spatially global feature vector.
2Diamentional Principle Component Analysis
Feature extraction is done based on 1D vector. Therefore the image matrix needs to
be transformed into vector.
6. PROPOSED SYSTEM
We propose “Facial Expression Identification”, the common facial feature is distance
between the eyes, width of the nose, check bones, jaws line chin and depth of the eyes sockets.
For processing on computer these features of a face have to be converted into numbers. The
set of numbers representing one face are compared with the numbers representing another
face.
8. IMPLEMENTATION
There are two method for achieve the detection of facial expression identification [1].
a) Training process.
b) Testing process.
a) Training process
1) Read faces which are stored in the training database.
2) All faces are normalize
3) Calculate the Eigen value
4) Calculate Eigen vectors
5) Obtain the Eigen face and the projection of the training images.
b) Testing process
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ISSN No: 2309-4893
International Journal of Advanced Engineering and Global Technology
I
Vol-03, Issue-05, May 2015
1) Read the face of the person from the database
2) Project the test image onto the face space.
3) Calculate the Euclidean distance
4) Train image with the minimum value of Euclidean distance.
5) It is assumed to fall in the same training set as that of face image.
9. CONCULSION AND FUTUREWORK
In this system we will implement face recognition system using Principle Component
Analysis (PCA) and Eigen face approach. The system will successfully recognize the human
face and facial expression detection for both male and female face work better in different
condition of face orientation. We will achieve excellent classification for all the emotions.
This is mainly because Principle Components have proven capability to provide significant
features and reduce the size of images.
REFERENCES
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Approach”, Thesis paper, Dept. of computer Science and Engineering, National Institute of
Technology, Rourkela.
[3] Mahesh Kumbhar, Ashish Jadhav and Manasi Patil,”Facial Expression Recognition
Based on Image Feature”, International Journal of Computer and Communication
Engineering, Vol.1,No.2, July 2012.
[4] DAW-TUNG LIN,” Facial Expression Classification Using PCA and Hierarchical
Radial Basis Function Network”, JOURNAL OF INFORMATION SCIENCE AND
ENGINEERING 22, 1033-1046 (2006).
[5] Saurabh P.Bahurupi,D.S.Chaudhari,”Principle Component Analysis for Face
Recognition”,
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[6] Kamal Dhanda, shalu Goel. “Enhancing the Recognition Rate and Reduce the
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Advanced Engineering and Global Technology. (IJAEGT). Vol.2 (10). 2014
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[7] Parvinder S. Sandhu, Iqbaldeep Kaur, Amit Verma, Samriti Jindal, Inderpreet Kaur,
Shilpi Kumari,” Face recognition Using Eigen face Coefficients and Principal Componenet
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