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EigenFaces - PowerPoint PPT Presentation

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EigenFaces. (squared) Variance. A measure of how "spread out" a sequence of numbers are. Covariance matrix. A measure of correlation between data elements. Example: Data set of size n Each data element has 3 fields: Height Weight Birth date. Covariance. [Collect data from class].

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Squared variance
(squared) Variance

  • A measure of how "spread out" a sequence of numbers are.

Covariance matrix
Covariance matrix

  • A measure of correlation between data elements.

  • Example:

    • Data set of size n

    • Each data element has 3 fields:

      • Height

      • Weight

      • Birth date


  • [Collect data from class]


  • The diagonals are the variance of that feature

  • Non-diagonals are a measure of correlation

    • High-positive == positive correlation

      • one goes up, other goes up

    • Low-negative == negative correlation

      • one goes up, other goes down

    • Near-zero == no correlation

      • unrelated

    • [How high depends on the range of values]


  • You can calculate it with a matrix:

    • Raw Matrix is a p x q matrix

      • p features

      • q samples

    • Convert to mean-deviation form

      • Calculate the average sample

      • Subtract this from all samples.

    • Multiply MeanDev (a p x q matrix) by its transpose (a q x p matrix)

    • Multiply by 1/n to get the covariance matrix.


  • [Calculate our covariance matrix]


  • An EigenSystem is:

    A vector (the eigenvector)

    A scalar λ(the eigenvalue)

  • Such that:

    (the zero vector isn't an eigenvector)

  • In general, not all matrices have eigenvectors.

Eigensystems and pca
EigenSystems and PCA

  • When you calculate the eigen-system of an n x n Covariance matrix you get:

    • n eigenvectors (each of dimension n)

    • n matching eigenvalues

  • The biggest eigen-value "explains" the largest amount of variance in the data set.


  • Say we have a 2d data set

    • First eigen-pair (v1 = [0.8, 0.6], λ=800.0)

    • Second eigen-pair (v2 = [-0.6, 0.8], λ=100.0)

    • 8x as much variance is along v1 as v2.

    • v1 and v2 are perpendicular to each other

    • v1 and v2 define a new set of basis vectors for this data set.



Conversions between basis vectors
Conversions between basis vectors

  • Let's take one data point…

    • Let's say it is [-1.5, 0.4] in "world units"

  • Project it onto v1 and v2 to get the coordinates relative to (v1, v2 unit-length basis vectors)



To convert back to "world units":

Pca and compression
PCA and compression

  • Example:

    • n (the number of features) is high (~100)

    • Most of the variance is captured by 3 eigen-vectors.

    • You can throw out the other 97 eigen-vectors.

    • You can represent most of the data for each sample using just 3 numbers per sample (instead of 100)

      • For a large data set, this can be huge.


  • Collect database images

    • Subject looking straight ahead, no emotion, neutral lighting.

    • Crop:

      • on the top include all of the eyebrows

      • on the bottom include just to the chin

      • on the sides, include all of the face.

    • Size to 32x32, grayscale (a limit of the eigen-solver)

    • In code, include a way to convert to (and from) a VectorN.

Eigenfaces cont
EigenFaces, cont.

  • Calculate the average image

    • Just pixel (Vector element) by element.

Eigenfaces cont1
EigenFaces, cont.

  • Calculate the Covariance matrix

  • Calculate the EigenSystem

    • Keep the eigen-pairs that preserve n% of the data variance (98% or so)

    • Your Eigen-database is the 32x32 average image and the (here) 8 32x32 eigen-face images.

Eigenfaces cont2
Eigenfaces, cont.

  • Represent each of your faces as a q-value vector (q = # of eigenfaces).

    • Subtract the average and project onto the q eigenfaces

    • The images I'm showing here are the original image and the 8-value "eigen-coordinates

9.08 187.4 -551.7 -114.4 -328.8 29.2 -371.9 -108.0

1277.0 150.9 -133.6 249.3 338.9 13.14 16.8 3.35

Eigenfaces cont3
EigenFaces, cont.

  • (for demonstration of compression)

    • You can reconstruct a compressed image by:

      • Start with a copy of the average image, X

      • Repeat for each eigenface:

        • Add the eigen-coord * eigenface to X

    • Here are the reconstructions of the 2 images on the last slide:



Eigenfaces cont4
EigenFaces, cont.

  • Facial Recognition

    • Take a novel image (same size as database images)

    • Using the eigenfaces computed earlier (this novel image is usually NOT part of this computation), compute eigen-coordinates.

    • Calculate the q-dimensional distance (pythagorean theorem in q-dimensions) between this image and each database image.

      • The database image with the smallest distance is your best match.