face recognition using neural network
DESCRIPTION
TRANSCRIPT
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
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