Face recognition using neural network

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  • 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

    1. 1. SEMINAR ON FACE RECOGNITION USING NEURAL NETWORK PRESENTED BY- INDIRA P NAYAK ROLL NO-29718 DEPT OF COMP SCI & ENGG IGIT,SARANG
    2. 2. CONTENT • Face Recognition • Neural Network • Steps • Algorithms • Advantages • Conclusion • References
    3. 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. 4. NEURAL NETWORK • It is a system of programs and data structures that approximates the operation of the human brain.
    5. 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. 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. 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. 8. PCA Algorithm •Step 1: Partition face images into sub-patterns
    9. 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. 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. 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. 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. 13. Contd…  MLPs: 1. Set of input layers 2. One or more hidden layers 3. Set of output layers
    14. 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. 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. 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

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