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Published on : Jan 08, 2015
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Slide 1 - Face Recognition Jeremy Wyatt
Slide 2 - Plan Eigenfaces: the idea Eigenvectors and Eigenvalues Co-variance Learning Eigenfaces from training sets of faces Recognition and reconstruction
Slide 3 - Eigenfaces: the idea Think of a face as being a weighted combination of some “component” or “basis” faces These basis faces are called eigenfaces -8029 2900 1751 1445 4238 6193 …
Slide 4 - Eigenfaces: representing faces These basis faces can be differently weighted to represent any face So we can use different vectors of weights to represent different faces -8029 -1183 2900 -2088 1751 -4336 1445 -669 4238 -4221 6193 10549
Slide 5 - Learning Eigenfaces Q: How do we pick the set of basis faces? A: We take a set of real training faces Then we find (learn) a set of basis faces which best represent the differences between them We’ll use a statistical criterion for measuring this notion of “best representation of the differences between the training faces” We can then store each face as a set of weights for those basis faces …
Slide 6 - Using Eigenfaces: recognition & reconstruction We can use the eigenfaces in two ways 1: we can store and then reconstruct a face from a set of weights 2: we can recognise a new picture of a familiar face
Slide 7 - Learning Eigenfaces How do we learn them? We use a method called Principle Components Analysis (PCA) To understand this we will need to understand What an eigenvector is What covariance is But first we will look at what is happening in PCA qualitatively
Slide 8 - Subspaces Imagine that our face is simply a (high dimensional) vector of pixels We can think more easily about 2d vectors Here we have data in two dimensions But we only really need one dimension to represent it
Slide 9 - Finding Subspaces Suppose we take a line through the space And then take the projection of each point onto that line This could represent our data in “one” dimension
Slide 10 - Finding Subspaces Some lines will represent the data in this way well, some badly This is because the projection onto some lines separates the data well, and the projection onto some lines separates it badly
Slide 11 - Finding Subspaces Rather than a line we can perform roughly the same trick with a vector Now we have to scale the vector to obtain any point on the line
Slide 12 - Eigenvectors An eigenvector is a vector that obeys the following rule: Where is a matrix, is a scalar (called the eigenvalue) e.g. one eigenvector of is since so for this eigenvector of this matrix the eigenvalue is 4
Slide 13 - Eigenvectors We can think of matrices as performing transformations on vectors (e.g rotations, reflections) We can think of the eigenvectors of a matrix as being special vectors (for that matrix) that are scaled by that matrix Different matrices have different eigenvectors Only square matrices have eigenvectors Not all square matrices have eigenvectors An n by n matrix has at most n distinct eigenvectors All the distinct eigenvectors of a matrix are orthogonal (ie perpendicular)
Slide 14 - Covariance Which single vector can be used to separate these points as much as possible? This vector turns out to be a vector expressing the direction of the correlation Here I have two variables and They co-vary (y tends to change in roughly the same direction as x)
Slide 15 - Covariance The covariances can be expressed as a matrix The diagonal elements are the variances e.g. Var(x1) The covariance of two variables is:
Slide 16 - Eigenvectors of the covariance matrix The covariance matrix has eigenvectors covariance matrix eigenvectors eigenvalues Eigenvectors with larger eigenvectors correspond to directions in which the data varies more Finding the eigenvectors and eigenvalues of the covariance matrix for a set of data is termed principle components analysis
Slide 17 - Expressing points using eigenvectors Suppose you think of your eigenvectors as specifying a new vector space i.e. I can reference any point in terms of those eigenvectors A point’s position in this new coordinate system is what we earlier referred to as its “weight vector” For many data sets you can cope with fewer dimensions in the new space than in the old space
Slide 18 - Eigenfaces All we are doing in the face case is treating the face as a point in a high-dimensional space, and then treating the training set of face pictures as our set of points To train: We calculate the covariance matrix of the faces We then find the eigenvectors of that covariance matrix These eigenvectors are the eigenfaces or basis faces Eigenfaces with bigger eigenvalues will explain more of the variation in the set of faces, i.e. will be more distinguishing
Slide 19 - Eigenfaces: image space to face space When we see an image of a face we can transform it to face space There are k=1…n eigenfaces The ith face in image space is a vector The corresponding weight is We calculate the corresponding weight for every eigenface
Slide 20 - Recognition in face space Recognition is now simple. We find the euclidean distance d between our face and all the other stored faces in face space: The closest face in face space is the chosen match
Slide 21 - Reconstruction The more eigenfaces you have the better the reconstruction, but you can have high quality reconstruction even with a small number of eigenfaces 82 70 50 30 20 10
Slide 22 - Summary Statistical approach to visual recognition Also used for object recognition Problems Reference: M. Turk and A. Pentland (1991). Eigenfaces for recognition, Journal of Cognitive Neuroscience, 3(1): 71–86.