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

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. Training Set
Same dimensions

3. Images -> Vectors

4. Normalizing the Training Set
-

5. Normalizing the Training Set

6. Normalizing the Training Set

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. Eigenvectors
Eigenvectors are orthogonal
Eigenvalue: length of eigenvector

9. Eigenfaces
Eigenvector
x
Normalized picture

10. Extracting features

11. Obtain weights
Σ
ψ
w w w w w w w1 2 3 4 6 n5
+
Ωn =
w1
w2
w3
wn
…
…

12. Recognition of a face
- =
Ωnew
=
w1
w2
w3
wn
…
=

13. Recognition of a face
Ωnew
Ω1
Ω2
Ω3
Ω4
Ω5

14. Recognition of a face
0
0.1
0.2
0.3
0.4
0.5
0.6
Distance
Distance
Threshold