1. Face recognition via sparse representation

2. Breakdown Problem Classical techniques New method based on sparsity Results

3. Classical Techniques Eigenfaces Uses PCA for feature extraction Problems faced Extremely intensive Poor results when there’s no frontal view Poor results with bad lighting Poor results with noise

4. Classical Techniques Support Vector Machines PCA for feature extraction Radial Basis function One versus all classifier Problems faced Extremely intensive Poor results with bad lighting Sensitive to noise

5. Via sparse representation Redundancy As the number of image pixels is far greater than the number of subjects that have generated the images Robustness from sparsity Identity of the test image Nature of occlusion

6. Problem A w x h image is identified as a vector v ϵ R m given by stacking columns A = [v 1 v 2 v 3 v 4,…..,v n ] ϵ R mxn A test image y = A i x i, assuming no occlusion where y = test image of the i th object

7. If ρ is the fraction of pixels occluded, y = y 0 + e = Ax 0 + e Problem statement: Given A 1, A 2, A 3,…., A k & y by sampling an image from the i th class & perturbing the values of ρ of its pixels arbitrarily, find the correct class.

9. Algorithm n training samples partitioned into k classes B = [A 1 A 1 ….A n I], normalize to have unit l 2 norm. ẃ 1 = arg min ||w|| 1 S.T Bw = y w Residuals r i (y) = ||y – Aδ i (ẋ 1 ) – ê 1 ||2 for i = 1,2,….k. Output = arg min i r i (y).

10. Dataset Extended Yale B dataset 38 subjects 717 images for training and 453 for testing

11. RESULTS

12. 1. Random pixel corruption

13. 2. Random block occlusion

14. Recognition despite disguise

15. THANK YOU