Face recognition is a challengngprob as it involves identifyng the image in ol types of environ lyk-in diff facial expression,diff lighting cond,facialaccessories,aging effects.
In our body neurons have the abilities to remember, think and apply previous experiences to our every action.synapses are the receieving or input units to which input are given.the summing unit computes the inner product between inputs and synapse’s weights(net inputs). After the summing unit there is a threshold that increases or reduces the net input. Then an activation function, f(I), that reduces the output variance of a neuron by mapping the thresholded net input generally within the interval [0; 1] or [-1; 1] after which we get the output.
It is abb of backward propagation of errors.it is a method of training artificial neural networks.ex-a child learns to identify a dog from ex of dogs.
The signal is generated in the input layer,propagated through the hidden layers until it reaches the output layer.
Face recognition using neural network
FACE RECOGNITION USING
INDIRA P NAYAK
DEPT OF COMP SCI & ENGG
• Face recognition involves comparing an
image with a database of stored faces in
order to identify the individual in that input
• Used in human-machine
interfaces, automatic access control
• It is a system of programs and data structures that
approximates the operation of the human brain.
• To reduce or eliminate some of the
variations in face due to illumination.
• It normalize and enhance the face image
to improve the recognition performance.
• By using the normalization process system
robustness against scaling, posture, facial
expression and illumination is increased.
• It involves a mathematical procedure that
transforms a number of possibly correlated
variables into a smaller number of
uncorrelated variables called principal
•Step 1: Partition face images into sub-patterns
• Step 2: Compute the expected contribution of
– Generate the Mean and Median faces for each
person, and use these “virtual faces” as the probe
set in training
– Use the raw face-image sub-patterns as the
gallery set in for training, and compute the PCA’s
projection matrix on these gallery set
– For each sample in the probe set, compute its
similarity to the samples in corresponding gallery
– If a sample from a sub-pattern’s probe set is
correctly classified, the contribution of this sub-
pattern is added by 1
Face images from AR face database, and the computed
• Step 3: Classification
When an unknown face image comes in
• partition it into sub-patterns
• classify the unknown sample’s identity in each
• Incorporate the expected contribution and the
classification result of all sub-patterns to
generate the final classification result
It trains the network to achieve a balance
between the ability to respond correctly to
the input patterns that are used for training
& the ability to provide good response to
the input that are similar.
It requires a dataset of the desired output
for many input, making up the training set.
These are necessarily Multilayer
1. Set of input layers
2. One or more hidden layers
3. Set of output layers
• When an element (Artificial neuron) of the
neural network fails, it can continue without
any problem by their parallel nature.
• A neural network learns and does not need to
• If there is plenty of data and the problem is
poorly understood to derive an approximate
model, then neural network technology is a
• Face recognition can be applied in
Security measure at Air ports, Passport
verification, Criminals list verification in
police department, Visa processing ,
Verification of Electoral identification and
Card Security measure at ATM’s.
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