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Face recognition using Eigenfaces

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Presentation explaining eigenfaces algorithm.

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Face recognition using Eigenfaces

1. 1. Face recognition using Eigenfaces (by Turk and Pentland, 1991) Explained by Juan Miguel Valverde Martínez More details: http://laid.delanover.com/explanation-face-recognition-using-eigenfaces
2. 2. Training Set Same dimensions
3. 3. Images -> Vectors
4. 4. Normalizing the Training Set -
5. 5. Normalizing the Training Set
6. 6. Normalizing the Training Set
7. 7. Covariance 235 235 235 x 235 1 x 55225 55225 55225 C = A A’ (C: 55225 x 55225) A: 55225 x 16 16 x 55225 & 55225 x 16 = 16 x 16
8. 8. Eigenvectors Eigenvectors are orthogonal Eigenvalue: length of eigenvector
9. 9. Eigenfaces Eigenvector x Normalized picture
10. 10. Extracting features
11. 11. Obtain weights Σ ψ w w w w w w w1 2 3 4 6 n5 + Ωn = w1 w2 w3 wn … …
12. 12. Recognition of a face - = Ωnew = w1 w2 w3 wn … =
13. 13. Recognition of a face Ωnew Ω1 Ω2 Ω3 Ω4 Ω5
14. 14. Recognition of a face 0 0.1 0.2 0.3 0.4 0.5 0.6 Distance Distance Threshold