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Face recognition using Eigenfaces
(by Turk and Pentland, 1991)
Explained by Juan Miguel Valverde Martínez
More details:
ht...
Training Set
Same dimensions
Images -> Vectors
Normalizing the Training Set
-
Normalizing the Training Set
Normalizing the Training Set
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 ...
Eigenvectors
Eigenvectors are orthogonal
Eigenvalue: length of eigenvector
Eigenfaces
Eigenvector
x
Normalized picture
Extracting features
Obtain weights
Σ
ψ
w w w w w w w1 2 3 4 6 n5
+
Ωn =
w1
w2
w3
wn
…
…
Recognition of a face
- =
Ωnew
=
w1
w2
w3
wn
…
=
Recognition of a face
Ωnew
Ω1
Ω2
Ω3
Ω4
Ω5
Recognition of a face
0
0.1
0.2
0.3
0.4
0.5
0.6
Distance
Distance
Threshold
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Face recognition using Eigenfaces

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Presentation explaining eigenfaces algorithm.

More information: http://laid.delanover.com/explanation-face-recognition-using-eigenfaces/

Published in: Engineering
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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

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