A Survey: Neural Network in the field of Face Recognition
System
Madhulata Pushpakar
Reshamlal Pradhan
MCA pursuing
MSIT Department
Mats University, Raipur
MTech
madhupushpakar6@gmail.co
m
ABSTRACT
Face recognition has been studied for many years and has
practical application in areas such as security systems,
detection of criminals and help with speech identification
system. The Face Recognition System is an automated
system to identify a human face and emotions. There are
several methods which are tested in an efficient way for facial
expression recognition purpose. The artificial neural network
is one of the most optimization techniques used for training
the networks for efficient recognition. In this paper an
overview of neural network in the field of face recognition
system is provided. A discussion outlining the neural network
for facial expression detection is provided with articles of
various authors. What should be the alternative classification
techniques for face recognition is also discussed.
Keywords
Feature classification, Face recognition, Feature extraction,
Neural Network, Feedback Neural Network, Feed forward
Neural Network.
1. INTRODUCTION
Face Recognition is a term that includes several subproblems. There are different classifications of these
problems in the bibliography. Some of them will be
explained on this section. Finally, a general or unified
classification will be proposed.
Face recognition has been studied for many years and has
practical application in areas such as security systems,
detection of criminals and help with speech identification
system. The Face Recognition System is a system that
automatically identifies a human face and emotions. The face
recognition problem is difficult problem for human because it
represents complex, multidimensional, meaningful visual
motivation. Face Recognition is important to human
because the face plays a major role in social intercourse,
conveying emotions and feelings. Biometric is the science
and machinery of recording and authenticate individuality
using physiological or behavioral characteristics of the
subject. It is a computable quality, whether physiological
or behavioral, of a living organism that can be used to
Mats University, Raipur
reshamlalpradhan6602@gm
ail.com
differentiate that organism as an individual. Biometric data is
captured when the user makes an try to be authenticated
by the system. This data is used by the biometric system for
real-time comparison against biometric samples. Biometrics
offers the identity of an individual may be viewed as the
information associated with that person in a particular identity
management system.
1.1 A generic face recognition system
A good face recognition system must be healthy to
overcome these difficulties and generalize over many
conditions to capture the essential similarities for a given
human face.
The input of a face recognition system is always an image or
video stream. The output is an identification or verification of
the subject or subjects that Appear in the image or video.
Some approaches define a face recognition system as a
three step process – see Figure:1.1. From this point of view,
the Face Detection and Feature Extraction phases could run
simultaneously.
Fig:1.1 Face recognition system
1.2 Face detection
Now a days some applications of Face Recognition don’t
require face detection. In some cases, face images stored in
the data bases are already normalized. There is a standard
image input format, so there is no need for a detection step.
An example of this could be a criminal data base. There, the
law enforcement agency stores faces of people with a criminal
report. If there is new subject and the police has his or her
passport photograph, face detection is not necessary.
However, the conventional input image of computer vision
systems are not that suitable. They can contain many items
or faces.
1.3 Face tracking
Many face recognition systems have a video sequence as the
input. Those systems may require to be capable of not only
detecting but tracking faces. Face tracking is essentially a
motion estimation problem. Face tracking can be performed
using many different methods, e.g., head tracking, feature
tracking, image-based tracking, model-based tracking. These
are different ways to classify these algorithms:

Head tracking/Individual feature tracking. The head
can be tracked a whole entity, or certain features
tracked individually.

2D/3D. Two dimensional systems track a face and
output an image space where the face is located.
Three dimensional systems, on the other hand,
perform a 3D modeling of the face. This approach
allows to estimate pose or orientation variations.
1.4 Feature Extraction
Humans can recognize faces since we are 5 year old. It seems
to be an automated and dedicated process in our brains,
though it’s a much debated issue . What it’s clear is that we
can recognize people we know, even when they are wearing
glasses or hats. We can also recognize men who have grown a
beard. It’s not very difficult for us to see our grandma’s
wedding photo and recognize her, although she was 23 years
old. All these processes seem trivial, but they represent a
challenge to the computers. In fact, face recognition’s core
problem is to extract information from photographs. This
feature extraction process can be defined as the procedure of
extracting relevant information from a face image.
every possible subset and choose the one that fulfills the
criterion function. However, this can become an unaffordable
task in terms of computational time. Some effective
approaches to this problem are based on algorithms like
branch and bound algorithms.
1.6 Face classification
Once the features are extracted and selected, the next step is to
classify the image. Appearance-based face
recognition
algorithms use a wide variety of classification methods.
Sometimes two or more classifiers are combined to achieve
better results. On the other hand, most model-based
algorithms match the samples with the model or template.
Then, a learning method is can be used to improve the
algorithm. One way or another, classifiers have abig impact in
face recognition. Classification methods are used in many
areas like data mining, finance, signal decoding, voice
recognition, natural language processing or medicine.
Classification algorithms usually involve some learning
supervised, Unsupervised or semi-supervised. Unsupervised
learning is the most difficult approach, as there are no tagged
examples. However, many face recognition applications
include a tagged set of subjects. Consequently, most face
recognition systems implement supervised learning methods.
There are also cases where the labeled data set is small.
Sometimes, the acquisition of new tagged samples can be
infeasible. Therefore, semi-supervised learning is required.
General Categorization of Feature classification Techniques
are:
Similarity based classifiers: Template Matching, Nearest
Mean, Subspace Method, 1-NN, K-NN, Self-Organizing
Maps(SOM).
Probabilistic based classifiers: Bayesian, Logistic Classifier,
Parzen Classifier.
Classifiers using judgment limitations: Neural Network,Fisher
Linear Discriminate (FLD), Binary Decision Tree, Perceptron,
Multi-layer Perceptron, Radial Basis Network, Support Vector
Machines.
Combiners can be grouped in three categories according to
their architecture:
Figure 1.2: Feature extraction processes.
Feature extraction involves several steps - dimensionality
reduction, feature extraction and feature selection.This steps
may overlap, and dimensionality reduction could be seen as a
consequence of the feature extraction
and selection
algorithms.



Parallel: All classifiers are executed separately. The
combiner is then applied.
Serial: Classifiers run one after another. Each classifier
polishes previous results.
Hierarchical: Classifiers are combined into a tree-like
structure.
1.7 Neural Network:
1.5 Feature selection methods
Feature selection algorithm’s aim is to select a subset of the
extracted features that cause the smallest classification error.
The importance of this error is what makes feature selection
dependent to the classification method used.The most
straightforward approach to this problem would be to examine
A neural network is a massively parallel distributed processor
that has a natural propensity for storing experiential
knowledge and making it available for use. It resembles the
brain in two respects:
1. Knowledge is acquired by the network through a learning
process.
2. Interneuron connection strengths known as synaptic
weights are used to store the knowledge.
 Neural networks are also referred to as neurocomputers,
connectionist networks, parallel distributed processors,
etc.
 Neural network is divided in two parts they are-Artificial
Neural Network and Biological Neural Network.
 A neural network is an Artificial representation of human
brain , that tries to simulate is learning process.
A Neural Network is a system that based on models of brain
structure. A neural network is a particularly equivalent
distributed system that a natural partiality for store observed
facts and makes it offered for use. Nodes are using for store
the facts. It is layer based model that means every layer
connected to each other. The layer is made by a number of
units that connected to each other and the node is hold the
activation energy .the neural network layer is: initial layer,
middle layer and result layer. The initial layer connects to
more than one middle layer and that layer sends to the data
into the middle layer. Middle layer is the main layer of the
NN that process the all received data and after processing
send to the result to the result layer that means the result
layer hold the result. Neural network is divided in two parts
they are-Artificial Neural Network and Biological Neural
Network.
system. An ANN is a system that contains hidden layers,
inputs and outputs. ANN is consisting of huge number of easy
processing units that are connecting to each other and layers.
An ANN consist the some functions for solving the problems.
ANN are obscene electronic model based on neural structure
of the brain. Artificial neuron is supposed to mimic the action
of a biological neuron.
ANN is used to Artificial Intelligence problem. An ANN is
configured for some specific application such as pattern
recognition and data classification through learning pattern.
1.7.1 Biological Neural Network
The BNN is consisting of a large number of neurons and one
neuron connected to other neurons. Neurons are a part of
BNN. The human brain having 100 billion neurons in
average. A neuron comprise in three parts: dendrites, soma
and axon. Dendrites are receives the signal from neurons.
Which is connected to one neuron. Axon is a very small
container that transmits the signal from one neuron to each
other.
Fig:1.4 Structure Of the ANN
ANN basically divided into two categories: Feed forward and
Feed backward Network.
Feed-forward networks:
Feed-forward ANNs allow signals to travel one way only;
from input to output. There is no feedback(loops) i. e. the
output of any layer does not affect that same layer. Feedforward ANNs tend to be straight forward networks that
associate inputs with outputs. They are extensively used in
pattern recognition. This type of organization is also referred
to as bottom-up or top-down.
Feedback Network:
Fig 1.3: Structure of the BNN
1.7.2 Artificial Neural Network
ANN is a computerized structure of the human brain. It
contains the mathematical model of biological nervous
Feedback networks can have signals travelling in both
directions by introducing loops in the network. Feedback
networks are very powerful and can get extremely
complicated. Feedback networks are dynamic ; their ‘state’
is changing continuously until they reach an equilibrium
point. They remain at the equilibrium point until the input
changes and a new equilibrium needs to be found. Feedback
architectures are also referred to as interactive or recurrent,
although the latter term is often used to denote feedback
connections in single-layer organizations.
2. LITERATURE SURVEY
In the recent years, artificial neural networks (ANN) were
used mostly for structure intelligent computer systems
related to face recognition and image processing , The
most popular ANN model is the back-propagation neural
network (BPNN) which can be educated using backpropagation training algorithm (BP). Different ANN models
were used widely in face recognition and many times they
used in combination with the above mentioned methods.
Some of recent works on face recognition system based on
neural network are:
Hayet Boughrara ·Mohamed Chtourou · Chokri Ben Amar ·
Liming Chen proposed “ Facial expression recognition based
on a mlp neural network using constructive training
algorithm” (2014)[1]. This paper presents a constructive
training algorithm for Multi Layer Perceptron (MLP) applied
to facial expression recognition applications. The developed
algorithm is composed by a single hidden-layer using a given
number of neurons and a small number of training patterns.
When the Mean Square Error MSE on the Training Data TD
is not reduced to a predefined value, the number of hidden
neurons grows during the neural network learning. In this
paper, a constructive training algorithm for MLP neural
networks has been proposed. Starting with a neural network
containing a given number of hidden neurons and a small
number of training patterns, the MLP neural network using
the back-propagation algorithm is trained. The hidden neurons
grow during the training when the MSE on the TD is not
reduced to a predefined value. Input patterns are trained
incrementally until all patterns of TD are selected and learned.
Sheela Shankar, 2v.R Udupi Proposed “Neural Networks In
Identifying Expressions In Face Recognition Systems”
(2014) [2]. This paper makes a study of the neural network
based approaches to identify expressions which aids in
fostering face recognition systems. Multilayer Perceptron and
Self Organizing Maps are the variants of neural networks,
which are discussed here in detail. The paper makes a
scrutinizing survey of neural network techniques to identify
various expressions in human face. It was found that neural
network based approach is quite robust to uniquely identify a
specific expression. MLP and SOM techniques are discussed
in detail. The also stated that survey can be used as a module
in face recognition systems to identify a person’s expressions
which are crucial to prove his identity in authentication
domains.
Harish Kumar Dogra1, Zohaib Hasan2, Ashish Kumar Dogra
proposed “ Face expression recognition using Scaledconjugate gradient Back-Propagation algorithm” (2013)[3].
In this paper they will study the latest work done that has been
done in the field of facial expression recognition and analysis.
In our work they have recognized six different expressions
using Cohn-kanade database and system is trained using
scaled conjugate gradient back-propagation algorithm. In
proposed methodology they have used MATLAB’s computer
vision toolbox for face detection & cropping the images and
neural network toolbox. This paper describes the different
techniques that are employed in face expression recognition
and analysis. With respect to machine learning techniques,
they noticed a strong trend to use SVMs. Most of the teams
result that they have already shown in Table 2 used SVM,
such techniques have proven very popular in recent literature.
But in work they have used scale conjugate gradient back
propagation algorithm and they are getting overall testing
accuracy up to 87.2% which is better than the as compared to
the work done using SVM explained in table 2. In our future
work, they can improve our recognition rate by using LBP
histogram, equalization techniques & PCA techniques before
training the system.
Pushpaja V. Saudagare, D.S. Chaudhari proposed “ Facial
Expression Recognition using Neural Network –An
Overview” (2012)[4 ]. This paper reviews various techniques
of facial expression recognition systems using MATLAB
(neural network) toolbox. Keywords: face recognition, neural
network, and facial expression recognition. In many face
recognition systems the important part is face detection. The
task of detecting face is complex due to its variability present
across human faces including color, pose, expression, position
and orientation. In this paper the automatic facial expression
recognition systems are overviewed. The neural network
approach is based on face recognition, feature extraction and
categorization. The aproach of facial expression recognition
method involve the optical flow method, active shape model
technique, principle componenet analysis algorithm (PCA)
and neural network technique. The approach does provide a
practical solution to the problem of facial expression
recognition and it can work well in constrained enviournment.
S.P.Khandait , Dr. R.C.Thool, P.D.Khandait proposed
“Automatic Facial Feature Extraction and Expression
Recognition based on Neural Network” (2011)[5]. In this
paper, an approach to the problem of automatic facial feature
extraction from a still frontal posed image and classification
and recognition of facial expression and hence emotion and
mood of a person is presented. Feed forward back propagation
neural network is used as a classifier for classifying the
expressions of supplied face into seven basic categories like
surprise, neutral, sad, disgust, fear, happy and angry. For face
portion segmentation and localization, morphological image
processing operations are used. Permanent facial features like
eyebrows, eyes, mouth and nose are extracted using SUSAN
edge detection operator, facial geometry, edge projection
analysis. In this paper, automatic facial expression recognition
(AFER) system is proposed. Machine recognition of facial
expression is a big challenge even if human being recognizes
it without any significant delay. The combination of SUSAN
edge detector, edge projection analysis and facial geometry
distance measure is best combination to locate and extract the
facial feature for gray scale images in constrained
environments and feed forward back-propagation neural
network is used to recognize the facial expression. 100%
accuracy is achieved for training sets and 95.26% accuracy is
achieved for test sets of JAFFE database which is promising.
Therefore in future an attempt can be made to develop hybrid
approach for facial feature extraction and recognition
accuracy can be further improved using same NN approach
and hybrid approach such as ANFIS. An attempt can also be
made for recognition of other database images or images
captured from camera.
Konrad Schindler a,_Luc Van Gool a,b, Beatrice de Gelder c
proposed “Recognizing emotions expressed by body pose: A
biologically inspired
neural model”( 2008) [6]. They
approach recognition of basic emotional categories from a
computational perspective. In keeping with recent
computational models of the visual cortex, they construct a
biologically plausible hierarchy of neural detectors, which can
discriminate seven basic emotional states from static views of
associated body poses. The model is evaluated against human
test subjects on a recent set of stimuli manufactured for
research on emotional body language. They have presented a
biologically inspired neural model for the form-perception of
emotional body language. When presented with an image
showing an expression of emotional body language, the model
is able to assign it to one out of seven emotional categories
(the six basic emotions + neutral). On the cognitive science
side, the study has two main conclusions: firstly, although
emotional body language is a rather complex phenomenon, an
important part of the categorization task can be solved with
low-level form processing alone, without recovering 3D pose
and motion. This means that for the perception of emotional
body language, 2D pose recognition, and more generally 2D
processing, could play a direct role (in all likelihood,
categorization would be even better when also using opticflow from the motion pathway, which we have not modeled).
George Caridakis , Kostas Karpouzis, Stefanos Kollias
proposed “User and context adaptive neural networks for
emotion recognition” (2008) [7]. An effective approach is
presented in this paper, which uses neural network
architectures to both detect the need for adaptation of their
knowledge, and adapt it through an efficient adaptation
procedure. An experimental study with emotion datasets
generate in the framework of the EC IST Humaine Network
of Excellence. In this paper They proposed an extension of a
neural network adaptation procedure, which caters for training
from different modalities. After training and testing on a
particular subject, the best-performing network is adapted
using prominent samples from discourse with another subject,
so as to adapt and improve its ability to generalize. Results
shown here indicate that the performance of the network is
improved using this approach, without the need to train a
specific network for each subject, which would wipe out the
nice generalization attribute of the network. Future work
includes the extension of this work to include speech-related
modalities, deployment on different naturalistic contexts and
introduction of mechanisms to handle uncertainty in the
various modalities and decide which of them would be the
more robust to depend upon for co-training [3,20].
N. Fragopanagos*, J.G. Taylor
proposed “Emotion
recognition in human–computer interaction”( 2005)[8]. They
outline the approach to construct an emotion- recognizing
system. It was based on guidance from psychological studies
of emotion, as well as from the nature of emotion in its
interaction with attention. The aim of project was to build an
automatic emotion recognition system able to exploit
multimodal emotional markers such as those embedded in the
voice, face and words spoken. They discussed the numerous
potential applications of such a system for industry as well as
in academia.
They presented an artificial neural network called
ANNA developed for the automatic classification of
emotional states driven by a multimodal feature input. The
novel feature of ANNA is the feedback attentional loop
designed to exploit the attention-grabbing effect of emotional
stimuli to further enhance and clarify the salient components
of the input stream. They also presented the results obtained
through the use of ANNA on training and esting material
based on the SALAS scenario developed within the ERMIS
framework. The results obtained by using ANNA indicate that
there can be crucial differences between subjects as to the
clues they pick up from others about the emotional states of
the latter.
Spiros V. Ioannou, Amaryllis T. Raouzaiou, Vasilis A.
Tzouvaras, Theofilos P. Mailis, Kostas C. Karpouzis, Stefanos
D. Kollias* proposed “Emotion recognition through facial
expression analysis based on a neurofuzzy network” (2005)
[9]. Extracting and validating emotional cues through analysis
of users’ facial expressions is of high importance for
improving the level of interaction in man machine
communication systems. Extraction of appropriate facial
features and consequent recognition of the user’s emotional
state that can be robust to facial expression variations among
different users is the topic of this paper. This paper describes
an emotion recognition system, which combines
psychological findings about emotion representation with
analysis and evaluation of facial expressions. The
performance of the proposed system has been investigated
with experimental real data. More specifically, a neurofuzzy
rule based system has been first created and used to classify
facial expressions using a continuous 2D emotion space,
obtaining high rates in classification and clustering of data to
quadrants of the emotion representation space. Future
extensions will include emotion recognition based on
combined facial and gesture analysis. These can provide the
means to create systems that combine analysis and synthesis
of facial expressions, for providing more expressive and
friendly interactions . Moreover, development of rulebased
emotion recognition provides the possibility to combine the
results obtained within the framework of the ERMIS project
with current knowledge technologies, e.g. in implementing an
MPEG-4 visual ontology for emotion recognition.
E.C. Laskaria,b,∗ , G.C. Meletiouc, D.K. Tasoulisa,b, M.N.
Vrahatisa,b “Studying the performance of artificial neural
networks on problems related to cryptography”(2005)[10].
Cryptosystems rely on the assumption that a number of
mathematical problems are computationally intractable, in the
sense that they cannot be solved in polynomial time.
Numerous approaches have been applied to address these
problems. In this paper, they consider artificial neural
networks and study their performance on approximation
problems related to cryptography. In this paper, they extend
previous results by studying the performance of ANNs on the
same problems but in a different setting. Concerning the DLP,
ANNs are used in the case where the prime number p and the
primitive element modulo p vary. Therefore the basic
algebraic structure (the finite field) changes. Concerning the
factorization problem for the RSA, in addition to _(N), an
other function is tested. Here they consider only artificial
feedforward neural networks. In a future correspondence they
intend to apply various other networks and learning
techniques including nonmonotone neural networks [2],
probabilistic neural networks[31], self-organized maps [10],
recurrent networks and radial basis function networks [8].
B. Fasela;∗ , Juergen Luettinb proposed “Automatic facial
expression analysis: a survey” (2002)[11]. In this survey, we
introduce the most prominent automatic facial expression
analysis methods and systems presented in the literature.
Facial motion and deformation extraction approaches as well
as classi0cation methods are discussed with respect to issues
such as face normalization, facial expression dynamics and
facial expression intensity, but also with regard to their
robustness towards environmental changes. Although facial
expressions often occur during conversations , none of the
cited approaches did consider this possibility. Ifautomatic
facial expression analysis systems are to be operated
autonomously, current feature extraction methods have to be
improved and extended with regard to robustness in natural
environments as well as independence ofmanual intervention
during initialization and deployment. In this survey, we have
reviewed the most prominent automatic facial expression
analysis methods and systems presented in the literature.
Various approaches have been made towards robust facial
expression recognition, applying di7erent image acquisition,
analysis and classi0cation methods. They concluded this
survey by summarizing recognition results and shortcomings
ofcurrently employed analysis methods and proposed possible
future research directions. Various applications using
automatic facial expression analysis can be envisaged in the
near future, fostering further interest in doing research in the
0elds of facial expression recognition, facial expression
interpretation and the facial expression animation.
Xudong Jiang ∗ , Alvin Harvey Kam Siew Wah proposed
“Constructing andtraining feed-forwardneural networks for
pattern classi$cation” (2002)[12]. A new approach of
constructing andtraining neural networks for pattern
classi$cation is proposed. Data clusters are generated
andtrained sequentially basedon distinct local subsets of the
training data. Obtainedclusters are then usedto construct a
feed-forwardnetwork, which is further trainedusing
standardalgorithms operating on the global training set. The
network obtained using this approach e6ectively inherits the
knowledge from the local training procedure before
improving on its generalization ability through the subsequent
global training. Various experiments demonstrate the
superiority of this approach over competing methods.
Keywords: Classi$cation; Neural networks; Clustering; Local
andglobal training; Generalization. The resulting LGT
network converges rapidly due to its inherited knowledge with
good generalization ability from the global training. They
believe proposed algorithm mimics the knowledge discovery
approach of the human brain, which typically decomposes a
complicated concept or idea into simpler subsets andlearning
the details of each subset sequentially before integrating
andgeneralizing all the individual pieces of knowledge
acquired. The e6ectiveness of the LGT network has been
amply demonstrated through its superior results in terms of
accuracy
andlearning
speed
(comparedwith
other
representative
clustering
andlearning
approaches
implementedin this work) on three benchmark problems
operating on both synthetic andreal data sets.
S. Dubuisson, F. Davoine, M. Masson* proposed “ A
solution for facial expression representation and
recognition”(2002)[13 ]. In this paper, they propose a feature
selection process that sorts the principal components,
generated by principal component analysis, in the order of
their importance to solve a specific recognition task. This
method provides a low-dimensional representation subspace
which has been optimized to improve the classification
accuracy. they focus on the problem of facial expression
recognition to demonstrate this technique. They also propose
a decision tree-based classifier that provides a ‘‘coarse-tofine’’ classification of new samples by successive projections
onto more and more precise representation subspaces this
study shows that choosing an optimal representation for faces
within the principal component approach can improve the
recognition task. Tests have proven quantitatively and
qualitatively the interest in sorting the principal components,
in the order of their importance for a recognition task, before
applying LDA. They have then proposed a decision tree
process that provides a ‘‘coarse to fine’’ classification,
increasing the classification accuracy by 5% for the six-facial
expression recognition problem.
Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu* proposed
“Forecasting with artificial neural networks:The state of the
art” (1997)[14]. This paper presents a state-of-the-art survey
of ANN applications in forecasting. Our purpose is to provide
(1) a synthesis of published research in this area, (2) insights
on ANN modeling issues, and (3) the future research
directions. Keywords: Neural networks; Forecasting. ANNs
offer a promising alternative approach to limitations of ANNs
and what they can do as well as traditional linear methods.
However, while ANNs what they cannot do. Several points
need to be provide a great deal of promises, they also embody
a emphasized: large degree of uncertainty. There are several
unsolved mysteries in this area. Since most results are · ANNs
are nonlinear methods per se. For static based on limited
empirical studies, the words linear processes with little
disturbance, they may ‘‘seem’’ and ‘‘appear’’ are used quite
commonly in not be better than linear statistical methods. the
literature. Few theoretical results are established · ANNs are
black-box methods. There is no explicit in this area. They
believe that the future of ANN forecasting will be even
brighter as more and time for training. more research efforts
are devoted to this area.
David Reby a, Sovan Lek b, Ioannis Dimopoulos c, Jean
Joachim a, Jacques Lauga c, Ste´phane Aulagnier a proposed
“ Artificial neural networks as a classification method in the
behavioural sciences” (1996)[15]. The classification and
recognition of individual characteristics and behaviours
constitute a preliminary step and is an important objective in
the behavioural sciences. Current statistical methods do not
always give satisfactory results. To improve performance in
this area, They present a methodology based on one of the
principles of artificial neural networks: the backpropagation
gradient. After summarizing the theoretical construction of the
model, they describe how to parameterize a neural network
using the example of the individual recognition of
vocalizations of four fallow deer (Dama dama). Keywords:
Mammal; Deer; Vocalization; Neural network; Classification;
Modelling. The first results presented here show that
performance of prediction was improved with an increasing
number of neurons in the hidden layer until an asymptote was
reached. Increasing this number further enhanced the risk of
overfitting (Gallant, 1993). The progressive improvement of
the prediction performance with the number of iterations is
obvious. A limit is also quickly reached and it is not
recommended to extend the training time. Indeed, time is
saved by avoiding overfitting.
3. CONCLUSION
Face recognition has received a great deal of attention over
the last few years because of its many applications in various
domains. In this paper the automatic facial expression
recognition systems using neural network are overviewed.
The neural network technique to detect Human Facial
expression and recognize them on the basis of accuracy and
computational time is discussed. The neural network approach
is based on face recognition, feature extraction and
categorization. The approach of facial expression recognition
method involve the feature extraction of images, feature
selection and then feature classification using different
classification techniques like neural network. The approach
does provide a practical solution to the problem of facial
expression recognition.
REFERENCES
[1] Hayet Boughrara ·Mohamed Chtourou · Chokri Ben Amar
·Liming Chen “ Facial expression recognition based on a mlp
neural network using constructive training algorithm” (2014).
[2] Sheela Shankar, V.R Udupi,(2014),” Neural Networks In
Identifying Expressions In Face Recognition Systems”,
International Journal of Industrial Electronics and Electrical
Engineering.
[3] Harish Kumar Dogra1, Zohaib Hasan2, Ashish Kumar
Dogra “ Face expression recognition using Scaled-conjugate
gradient Back-Propagation algorithm” (2013).
[4] Pushpaja V. Saudagare, D.S. Chaudhari
Expression Recognition
Overview” (2012).
using
Neural
“ Facial
Network –An
[5] S.P.Khandait, Dr. R.C.Thool, P.D.Khandait,(2011),”
Automatic Facial Feature Extraction and Expression
Recognition based on Neural Network”, (IJACSA)
International Journal of Advanced Computer Science and
Applications.
[6] Konrad Schindler a, Luc Van Gool a,b, Beatrice de Gelder
c “Recognizing emotions expressed by body pose: A
biologically inspired neural model”( 2008).
[7] George Caridakis , Kostas Karpouzis, Stefanos Kollias
proposed “User and context adaptive neural networks for
emotion recognition” (2008)
[8] N. Fragopanagos*, J.G. Taylor (2005)” Emotion
recognition in human–computer interaction”, Elsevier.
[9] Spiros V. Ioannou, Amaryllis T. Raouzaiou, Vasilis A.
Tzouvaras, Theofilos P. Mailis, Kostas C. Karpouzis, Stefanos
D. Kollias*,(2005),” Emotion recognition through facial
expression analysis based on a neurofuzzy network”, Elsevier.
[10] E.C. Laskaria,b,∗ , G.C. Meletiouc, D.K. Tasoulisa,b,
M.N. Vrahatisa,b “Studying the performance of artificial
neural networks on problems related to cryptography”(2005).
[11] B. Fasela;∗, Juergen Luettinb
“Automatic facial
expression analysis: a survey” (2002).
[12] Xudong Jiang ∗ , Alvin Harvey Kam Siew Wah
“Constructing andtraining feed-forwardneural networks for
pattern classi$cation” (2002).
[13] S. Dubuisson, F. Davoine, M. Masson* “ A solution for
facial expression representation and recognition”(2002).
[14] Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu*
“Forecasting with artificial neural networks:The state of the
art” (1997).
[15] David Reby a, Sovan Lek b, Ioannis Dimopoulos c, Jean
Joachim a, Jacques Lauga c, Ste´phane Aulagnier a
“Artificial neural networks as a classification method in the
behavioural sciences” (1996).
[16]Vaibhavkumar J. Mistry, Mahesh M. Goyani,(2013),” A
literature survey on Facial Expression Recognition using
Global Features”, International Journal of Engineering and
Advanced Technology (IJEAT).
[18]O.S. Eluyode and Dipo Theophilus Akomolafe,(2013),”
Comparative study of biological and artificial neural
networks”, European Journal of Applied Engineering and
Scientific Research.
[19]Surbhi, Mr. Vishal Arora,(2013),” The Facial expression
detection from Human Facial Image by using neural
network”, International Journal of Application or Innovation
in Engineering & Management (IJAIEM).
[20] N. Revathy, T.Guhan,” Face Recognition System Using
Back Propagation Artificial Neural Networks”, International
Journal of Advanced Engineering Technology.
[21] Omaima N. A. AL-Allaf, Abdelfatah Aref Tamimi,
Mohammad A. Alia,(2013),” Face Recognition System Based
on Different Artificial Neural Networks Models and Training
Algorithms”, (IJACSA) International Journal of Advanced
Computer Science and Applications.
[22] Mr. Dinesh Chandra Jain, Dr. V. P. Pawar,(2012),” A
Novel Approach For
Recognition Of Human Face
Automatically Using Neural Network Method”, International
Journal of Advanced Research in Computer Science and
Software Engineering.
[24] Renu Nagpal, Pooja Nagpal, Sumeet Kaur,(2010),”
Hybrid Technique for Human Face Emotion Detection”,
(IJACSA) International Journal of Advanced Computer
Science and Applications.