This document summarizes a seminar presentation on face recognition using neural networks. It discusses face recognition, neural networks, the steps involved which include pre-processing, principle component analysis, and back propagation neural networks. Advantages of neural networks for face recognition are robustness to variations in faces and ability to learn from data. Face recognition has applications in security and identification.

1. SEMINAR ON
FACE RECOGNITION USING
NEURAL NETWORK
PRESENTED BY-
INDIRA P NAYAK
ROLL NO-29718
DEPT OF COMP SCI & ENGG
IGIT,SARANG

2. CONTENT
• Face Recognition
• Neural Network
• Steps
• Algorithms
• Advantages
• Conclusion
• References

3. FACE RECOGNITION
• Face recognition involves comparing an
image with a database of stored faces in
order to identify the individual in that input
image.
• Used in human-machine
interfaces, automatic access control
system.

4. NEURAL NETWORK
• It is a system of programs and data structures that
approximates the operation of the human brain.

5. STEPS
• Pre-Processing stage
• Principle Component Analysis
• Back Propagation Neural Network
Pre-Processed
Input Image
Principle
Component
Analysis
Back
Propagation
Neural Network
Classified
Output
Image

6. Pre-Processing
• 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.

7. PRINCIPLE COMPONENT
ANALYSIS(PCA)
• It involves a mathematical procedure that
transforms a number of possibly correlated
variables into a smaller number of
uncorrelated variables called principal
components.

8. PCA Algorithm
•Step 1: Partition face images into sub-patterns

9. PCA Algorithm
• Step 2: Compute the expected contribution of
each sub-pattern
– 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
set

10. PCA Algorithm
– 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
contribution matrix

11. PCA Algorithm
• Step 3: Classification
When an unknown face image comes in
• partition it into sub-patterns
• classify the unknown sample’s identity in each
sub-pattern
• Incorporate the expected contribution and the
classification result of all sub-patterns to
generate the final classification result

12. BACK-PROPAGATION
NEURAL NETWORK(BPNN)
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
Perceptrons(MLPs).

13. Contd…
 MLPs:
1. Set of input layers
2. One or more hidden layers
3. Set of output layers

14. Advantages
• 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
be reprogrammed.
• If there is plenty of data and the problem is
poorly understood to derive an approximate
model, then neural network technology is a
good choice.

15. CONCLUSION
• 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.

16. REFERENCES
• www.cscjournals.org/csc/manuscript/Journals/SPIJ/.../S
PIJ-37.pdf
• http://www.uk.research.att.com/facedatabase.html
• http://cvc.yale.edu/projects/yalefaces/yalefaces.html
• http://www.dti.unimi.it/biolab/databases.htm
• citeseerx.ist.psu.edu/viewdoc/download?doi...1... -
United States
• www.wikipedia.com/Backpropagation.htm