CSE 185 Introduction to Computer Vision Face Recognition

The space of all face images When viewed as vectors of pixel values, face images are extremely high-dimensional –100x100 image = 10,000 dimensions However, relatively few 10,000-dimensional vectors correspond to valid face images We want to effectively model the subspace of face images

The space of all face images We want to construct a low-dimensional linear subspace that best explains the variation in the set of face images

Covariance Covariance is a measure of the extent to which corresponding elements from two sets of ordered data move in the same direction

Covariance (Variance-Covariance) matrix Variance-Covariance Matrix: Variance and covariance are displayed together in a variance-covariance matrix. The variances appear along the diagonal and covariances appear in the off- diagonal elements

Principal component analysis The direction that captures the maximum covariance of the data is the eigenvector corresponding to the largest eigenvalue of the data covariance matrix Furthermore, the top k orthogonal directions that capture the most variance of the data are the k eigenvectors corresponding to the k largest eigenvalues

Linear subspaces Classification can be expensive: –Big search prob (e.g., nearest neighbors) or store large PDF’s Suppose the data points are arranged as above –Idea—fit a line, classifier measures distance to line convert x into v 1, v 2 coordinates What does the v 2 coordinate measure? What does the v 1 coordinate measure? - distance to line - use it for classification—near 0 for orange pts - position along line - use it to specify which orange point it is

Dimensionality reduction We can represent the orange points with only their v 1 coordinates ( since v 2 coordinates are all essentially 0) This makes it much cheaper to store and compare points A bigger deal for higher dimensional problems

Linear subspaces Consider the variation along direction v among all of the orange points: What unit vector v minimizes var? What unit vector v maximizes var? Solution: v 1 is eigenvector of A with largest eigenvalue v 2 is eigenvector of A with smallest eigenvalue

Principal component analysis Suppose each data point is N-dimensional –Same procedure applies: –The eigenvectors of A define a new coordinate system eigenvector with largest eigenvalue captures the most variation among training vectors x eigenvector with smallest eigenvalue has least variation –We can compress the data using the top few eigenvectors corresponds to choosing a “linear subspace” –represent points on a line, plane, or “hyper-plane” these eigenvectors are known as the principal components

The space of faces An image is a point in a high dimensional space –An N x M image is a point in R NM –We can define vectors in this space as we did in the 2D case + =

Eigenfaces: Key idea Assume that most face images lie on a low-dimensional subspace determined by the first k (k<d) directions of maximum variance Use PCA to determine the vectors or “eigenfaces” u 1,…u k that span that subspace Represent all face images in the dataset as linear combinations of eigenfaces M. Turk and A. Pentland, Face Recognition using Eigenfaces, CVPR 1991Face Recognition using Eigenfaces

Eigenfaces example Training images x 1,…,x N

Eigenfaces example Top eigenvectors: u 1,…u k Mean: μ

Eigenfaces example Principal component (eigenvector) u k μ + 3σ k u k μ – 3σ k u k

Eigenfaces example Face x in “face space” coordinates: =

Eigenfaces example Face x in “face space” coordinates: Reconstruction: =+ µ + w 1 u 1 +w 2 u 2 +w 3 u 3 +w 4 u 4 + … = ^ x=

Recognition with eigenfaces Process labeled training images: 1.Find mean µ and covariance matrix Σ 2.Find k principal components (eigenvectors of Σ) u 1,…u k 3.Project each training image x i onto subspace spanned by principal components: (w i1,…,w ik ) = (u 1 T (x i – µ), …, u k T (x i – µ)) Given novel image x: 1.Project onto subspace: (w 1,…,w k ) = (u 1 T (x – µ), …, u k T (x – µ)) 2.Optional: check reconstruction error x – x to determine whether image is really a face 3.Classify as closest training face in k-dimensional subspace

Eigenfaces PCA extracts the eigenvectors of A –Gives a set of vectors v 1, v 2, v 3,... –Each vector is a direction in face space what do these look like?

Projecting onto the eigenfaces The eigenfaces v 1,..., v K span the space of faces –A face is converted to eigenface coordinates by

Recognition with eigenfaces Algorithm 1.Process the image database (set of images with labels) Run PCA—compute eigenfaces Calculate the K coefficients for each image 2.Given a new image (to be recognized) x, calculate K coefficients 3.Detect if x is a face 4.If it is a face, who is it? Find closest labeled face in database –nearest-neighbor in K-dimensional space

Choosing the dimension K KNMi = eigenvalues How many eigenfaces to use? Look at the decay of the eigenvalues –the eigenvalue tells you the amount of variance “in the direction” of that eigenface –ignore eigenfaces with low variance

Limitations Global appearance method: not robust to misalignment, background variation

Limitations PCA assumes that the data has a Gaussian distribution (mean µ, covariance matrix Σ) The shape of this dataset is not well described by its principal components

Limitations The direction of maximum variance is not always good for classification