Face Recognition and Detection using Neural
Vinita Bhandiwad, Assistant Professor,
Department of Information technology, Vidyalankar
Institute of technology, Mumbai
Bhanu Tekwani, Assistant Professor, Department
of Information technology, Vidyalankar Institute of
Abstract: Face recognition is one of the latest technology
being studied area in biometric as it has wide area of
applications. But Face detection is one of the
challenging problems in Image processing. The basic
aim of face detection is determine if there is any face in
an image & then locate position of a face in an image.
Evidently face detection is the first step towards
creating an automated system which may involve other
face processing. The neural network is created &
trained with training set of faces & non-faces. All results
are implemented in MATLAB 2013 environment.
Keywords: Face recognition; image processing;
MATLAB; Neural network.
The demand for personal identification in
computerized access control has resulted in an
increased interest in biometrics to replace passwords
and identification card. They can be easily breached
since the passwords can be divulged to an
unauthorized user and ID card can be stolen.
Biometrics which makes use of human features such
as iris, retina, face etc can be used to verify person’s
identity. The face recognition system has the benefit
of being a passive, non-intrusive system for verifying
personal identity. The proposed face recognition
system consists of face verification and face
recognition task. In verification task, the system
known a priori the identity of the user and has to
verify this identity i.e. the system has to decide
whether the a priori user is an imposter or not. It is
often useful to have a machine perform pattern
recognition. In particular machines which can read
face images are very cost effective. Therefore this
kind of application saves time and money and
eliminates the requirement that a human perform
such a repetitive task.
II. Why neural network
It has a feature of adaptive learning i.e. an ability to
learn how to do tasks. Also it can create its own
organization. It has a remarkable ability to derive
meaning from complicated or imprecise data. Today
neural networks are occurring everywhere. ANN are
relatively crude electronic models based on the neural
structure of the brain. Computers do rote things well
like keeping ledgers or performing complex math.
But computers have trouble recognizing even simple
pattern. The research shows that brain stores
information as patterns. Some of the patterns are
complicated and allows us the ability to recognize
individual faces from many different angles.
Basically all ANN have a similar structure or
topology. In the structure some of the neurons
interface to the real world to receive its input. The
output might be the particular character that the
network thinks it has scanned or the particular image
it thinks is being viewed. All the rest of the neurons
are hidden from view.
But a neural network is more than a bunch of
neurons. One of the easiest ways to design a structure
is to create layers of elements.
III. Different type of connection
There are basically two types of connections in neural
network they are feedforward neural network and
backpropagation neural network.
Backpropagation neural network: BPNN is the
most popular and oldest supervised learning
multiplayer feed forward neural network algorithm. It
is based on high mathematical foundation and has
very good application potential such as pattern
recognition, dynamic modelling, sensitive analysis
etc. The back propagation is the best known and
widely used learning algorithm in training multilayer
perceptron. The MLP refers to the network consisting
of a set of sensory units that constitute the input layer,
one or more hidden layers, and an output layer. The
input signal propagates through the network in
forward condition, from left to right and on a layer by
layer basis. The BPNN provides a computationally
efficient method for changing the weights in feed
forward network. The main aim is to train the
network to achieve a balance between the ability to
respond correctly the input pattern that are used for
training and ability to provide good response to the
input that are similar. As all techniques possesses
backpropagation too has its pros and cons and has its
problems such as slow convergence rate & problem
to get stuck in local minima however it is known for
its accuracy. Backpropagation was less to be used
because of its time length needed to train the network
to achieve the best result possible.
Feedforward neural network: In feedforward NN
the information flow is unidirectional. A unit sends
information to other unit from which it does not
receive any information. There are no feedback loops.
They are used in pattern generation/recognition/
classification. They have fixed inputs and outputs.
Here feedback loops are allowed they are used in
content addressable memories. A multilayer
feedforward neural network (MLFFNN) consists of
an input layer, hidden layer and an output layer of
neurons. Every node in a layer is connected to every
other node in neighbouring layer. A FFNN has no
memory and output is solely determined by current
input and weight values. A feedforward neural
network consists of one or more layers of usually
non-linear processing units. The output of each layer
serves as input to the next layer. The objective of
training a NN is to produce desired output when a set
of input is applied to the network.
IV. ANN Structure
A layer with n inputs Xi and corresponding weights
Wji (i=1,2 ,......n) function ∑ sums the n weighted
input and passes the result through a non linear
function ø(.) called activation function.
The function ø processes the adding results plus a
threshold value θ thus producing the output Y.
ANN is very well known powerful and robust
classification technique that has been used to
approximate real valued functions. ANN has been
used in many areas such as interpreting visual scenes,
speech recognition etc.
V. Experiment Results.
In this experiment where the images taken for
detection and recognition is from ORL database.
ORL stands for Olivette Research Laboratory
Database which is of size 3.3 Mb. It consists of total
400 images in database out of which 188 images are
used for testing purpose.
The training set is created based on this 188 images
whose complete data is been stored in the network.
Once training set is created then the test input is taken
whose initially all data is extracted using inbuilt
functions. Once the data is extracted it is matched
with the training set. Below fig shows the stage wise
Above fig shows GUI where on left hand side is the
input to be detected and matched. On the right hand
side we are comparing the test input with the
database input each part of the face is tested
separately for ex: eye co-ordinates, nose point, mouth
region etc. The data from each region is noted
separately and stored in database which will be useful
After comparing the test input faces with the database
images it displays the result as match with the image.
Above fig indicated the final result of neural network
where there are 26 input layers, 10 hidden layers and
1 output layer.
After performing the experiment it can be concluded
that face detection and recognition works very well
with neural networks because even though the face is
not proper it can be detected precisely because of
hidden layer processing. Therefore all the images in
the database have been tested and obtained exact
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