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Face Recognition using Neural Network by Dong Hyun Roh Computer Science Department KAIST

Face Recognition using Neural Network by Dong Hyun Roh Computer Science Department KAIST. content. face recognition related researches my system experiments analysis future work references. face recognition. What is face recognition?. face information for each person

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Face Recognition using Neural Network by Dong Hyun Roh Computer Science Department KAIST

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  1. Face Recognition using Neural Network by Dong Hyun Roh Computer Science Department KAIST

  2. content • face recognition • related researches • my system • experiments • analysis • future work • references

  3. face recognition • What is face recognition? face information for each person in a fixed domain a person whose face is in the input image face recognition system

  4. related researches • Template matching • very simple, very sensitive to noise • PCA(associative memory) • use PCA to extract important features and to reduce the dimension of the input data • Image compression(back propagation network) • use compression network to extract important features and to reduce the dimension of the input data • this is my approach • Others • extract visual feature like edges, shape of each components, etc.

  5. My system • Object • face recognition for the face image domain in which there are some small variations • face Image domain • 15 persons • 12 images per person • variation such as expression, direction of light, noise, glasses cen gla hap lef nog noi nor rig sad sle sur win

  6. My system • Procedure Person 1 Hidden nodes of Compression network Normalization Recognition network

  7. My system • Normalization • original image : 320*243 • normalized image : 32*24 • block size : 10*10 • averaging the intensity of pixels in block • compression network • input = raw data of image • output = raw data of image ( same as input ) • 32*24 - 40 - 32*24 structure • backpropagation learning algorithm

  8. My system • recognition network • input = values of hidden layer of compression network • output = a person whose face is in the image • 40 - 10 - 15 structure • backpropagation learning algorithm

  9. Experiment 1 • Compression network • training data • nor images of 15 persons • 2000 trainings, learning rate = 0.005 • result(sample) Training image nor cen gla hap lef nog noi rig sad sle sur win

  10. Experiment 2 • Recognition network • use the result of experiment 1 • training data • nor images of 15 persons • 2000 trainings, learning rate = 0.05 • result • total face image 180 ( including the training data) • the rate of correct recognition = 133/180*100 = 73.9% • the distribution of errors

  11. Experiment 3 • Recognition network • not use the result of experiment 1 • assign random 40 dimensional key to each image • training data • nor image of 15 persons • 2000 trainings, learning rate = 0.05 • result • total face image 180 ( including the training data) • the rate of correct recognition =12/180*100 = 6.7% • the distribution of errors

  12. Experiment 4 • Recognition network • not use compression network • use raw image(32*24) for training and test • training data • nor image of 15 persons • 2000 trainings, learning rate = 0.05 • 32*24 - 30-15 structure • result • total 180 images, the rate of correct recognition = 6.1% • the distribution of errors

  13. Analysis • The hidden of compression network • encode the inputs in a smaller dimensional subspace that retains most of the important information • if the hidden units are linear, the best solution to this problem is the least squares solution(i.e. to have the hidden units span the L principle components with the highest eigenvalues) • Cottrell et al. Found that the weights of 16 hidden units span the space of the 13 first engenvectors of he covariance matrix of the inputs. • when transformed to gray scale and graphically displayed, the hidden unit receptive and projective fields looked “face-like” and showed some similarity to the eigenvectors or eigenfaces The hidden value of compression network is very useful in face recognition!!

  14. Further work • Improvement of the performance of face recognition • improvement of the compression network • If the compression network is trained with all face images, what will be different from experiment 2 ? • The performance of compression network • The performance of recognition network

  15. Reference • Dominque Valentin, Herve Abdi, Alice J. O’Toole, Garrison W. Cottrell, “Connectionist Models of Face Processing: A survey”, 1994

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