Face recognition via sparse representation
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Transcript of Face recognition via sparse representation
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Face recognition via sparse representation
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Breakdown• Problem • Classical techniques• New method based on sparsity• Results
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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
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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
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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
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Problem• A w x h image is identified as a vector v ϵ Rm
given by stacking columns• A = [v1 v2 v3 v4,…..,vn] ϵ R mxn
• A test image y = Aixi, assuming no occlusion
where y = test image of the ith object
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• If ρ is the fraction of pixels occluded, • y = y0 + e = Ax0 + e
Problem statement:
Given A1, A2, A3,…., Ak & y by sampling an image from the ith class & perturbing the values of ρ of its pixels arbitrarily, find the correct class.
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• ẋ2 = arg min || y – Ax ||2X
• Error is non-Gaussian so this can give a lot of erroneous results
• Exploit sparsity of residue:• X0 = arg min || y – Ax ||0
X
• l1 is same as l0, sometimes.
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Algorithm• n training samples partitioned into k classes• B = [A1 A1….An I], normalize to have unit l2 norm.
• ẃ1 = arg min ||w||1 S.T Bw = y
w
• Residuals ri(y) = ||y – Aδi(ẋ1) – ê1||2 for i = 1,2,….k.
• Output = arg mini ri(y).
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Dataset• Extended Yale B dataset• 38 subjects• 717 images for training and 453 for testing
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RESULTS
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1. Random pixel corruption
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2. Random block occlusion
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Recognition despite disguise
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THANK YOU