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EigenFaces. (squared) Variance Covariance matrix A measure of correlation between data elements. Example: – Data set of size n – Each data element has.

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Presentation on theme: "EigenFaces. (squared) Variance Covariance matrix A measure of correlation between data elements. Example: – Data set of size n – Each data element has."— Presentation transcript:

1 EigenFaces

2 (squared) Variance

3 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

4 Covariance [Collect data from class]

5 Covariance

6 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]

7 Covariance 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.

8 Covariance [Calculate our covariance matrix]

9 EigenSystems

10 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.

11 Example 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. v1 v2

12 Conversions between basis vectors v1 v2

13 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.

14 EigenFaces 1.Collect database images a.Subject looking straight ahead, no emotion, neutral lighting. b.Crop: i.on the top include all of the eyebrows ii.on the bottom include just to the chin iii.on the sides, include all of the face. c.Size to 32x32, grayscale (a limit of the eigen-solver) d.In code, include a way to convert to (and from) a VectorN.

15 EigenFaces, cont. 2.Calculate the average image a.Just pixel (Vector element) by element.

16 EigenFaces, cont. 3.Calculate the Covariance matrix 4.Calculate the EigenSystem a.Keep the eigen-pairs that preserve n% of the data variance (98% or so) b.Your Eigen-database is the 32x32 average image and the (here) 8 32x32 eigen-face images.

17 Eigenfaces, cont. 5.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

18 EigenFaces, cont. 6.(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: OriginalReconstruction

19 EigenFaces, cont. 7.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.


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