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As applied to face recognition. Principal Components Analysis. video. Face Recognition. Detection vs. Recognition. Face Recognition. Identification vs. Verification. Face Recognition. Components: Face Detection Face Alignment Feature Extraction Matching. Face Recognition. Components:
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As applied to face recognition Principal Components Analysis
Face Recognition • Detection vs. Recognition
Face Recognition • Identification vs. Verification
Face Recognition • Components: • Face Detection • Face Alignment • Feature Extraction • Matching
Face Recognition • Components: • Face Detection • Face Alignment • Feature Extraction • Matching
Face Recognition • Dimensionality Reduction
Principal Components Analysis • “Eigenface” analysis
Principal Components Analysis Unordered Observations Temp. Light
Principal Components Analysis • Turns 4096 dimensions -> 40 or less dimensions
Principal Components Analysis Eigenvector 1 Eigenvector 2 Eigenvalues
Whats an eigenvector? • “Characteristic”
Whats an eigenvector? • “Characteristic” • Vector characterizing a feature of the matrix
Whats an eigenvector? • “Characteristic” • Vector characterizing a feature of the matrix • Eigenvalue = strength
Principal Components Analysis Eigenvalues Eigenvector 1 Eigenvector 2
Eigenfaces • [0,0,0,127, 55, 234, 255, 123, 98… n] • n = width * height
Eigenfaces Image1 Image2 Image3 Image4
Eigenfaces Average
Eigenfaces Eigenvalues Principal component Eigenvectors
Eigenfaces • Animation of reconstruction
.5 .2 .1 .03 .005