SEMINAR ONFACE RECOGNITION USING

NEURAL NETWORK

PRESENTED BY-

INDIRA P NAYAK

ROLL NO-29718

DEPT OF COMP SCI & ENGG

IGIT,SARANG

CONTENT

• Face Recognition• Neural Network• Steps• Algorithms• Advantages• Conclusion• References

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.

NEURAL NETWORK• It is a system of programs and data structures that

approximates the operation of the human brain.

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

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.

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.

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

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

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

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

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).

Contd…

MLPs:

1. Set of input layers

2. One or more hidden layers

3. Set of output layers

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.

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.

REFERENCES

• www.cscjournals.org/csc/manuscript/Journals/SPIJ/.../SPIJ-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

THANK U

ANY QUES