1. Face Recognition ECE 847 Pavan Kumar Y Digital Image Processing Bhanu Durga P

2. Acknowledgment Dr. Birchfield and Georgia Institute of Technology (whose database is used as the training set)

3. What is face recognition?

4. Applications • Criminal identification • Security systems • Image and film processing • Human-computer interaction

5. Different Algorithms • Eigenface based facial recognition • Fisherface based facial recognition • Hidden Markov model & dynamic link matching • 3-D facial recognition

6. Eigenface based face recognition Training • Set of Images (training set) is acquired. • Features (eigenvectors) are extracted • Highest M values used. • Project the face images onto the face space • M-dimensional weight space is found for each image • Weights and eigenfaces stored

7. Eigen faces

9. Recognition • Weights of test image calculated • Nearest neighbors in weight space found out

10. Pseudo code Feature Extraction: • % merge column vector for each training face X = [x1 x2 ... xm] • % compute the average face me = mean(X,2) A = X - [me me ... me] • % avoids N^2 by N^2 matrix computation of [V,D]=eig(A*A') % only computes M columns of U: A=U*E*V‘ • [U,E,V] = svd(A,0) • eigVals = diag(E) • lmda = eigVals(1:Mp) • % pick face-space principal components (eigenfaces) • P = U(:,1:Mp) • % store weights of training data projected into eigenspace • train_wt = P'*A

11. Results

14. Fisherface Algorithm • Smaller training set • Uses scatter matrices • In class scatter matrix • Between class scatter matrix • Finds the ratio of scatter matrices • Maximizes this ratio • Projects faces along this weights

15. Conclusions • Eigenface based approach • Simpler • Larger training set • Sensitive to lighting • Fisherface based approach • More complex • Uses Eigenface results • Invariant to lighting • Smaller training set

16. Thank You