Brendan J. Frey, Antonio Colmenarez and Thomas S. Huang 1998.
Mixtures of local linear subspaces for face recognition.
In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 1998,
Santa Barbara, CA.
IEEE Computer Society Press: Los Alamitos, CA.
Traditional subspace methods for face recognition compute a measure
of similarity between images after projecting them
onto a fixed linear subspace that is
spanned by some principal component vectors (a.k.a. "eigenfaces") of a
training set of images.
By supposing a parametric Gaussian distribution over the
subspace and a symmetric Gaussian noise model for the image given a
point in the subspace, we can endow this framework with a probabilistic
interpretation so that Bayes-optimal decisions can be made.
However, we expect that different image clusters (corresponding, say,
to different poses and expressions) will be best represented
by different subspaces.
In this paper, we study the recognition performance of
a mixture of local linear subspaces model that can be
fit to training data using the expectation maximization algorithm.
The mixture model outperforms a nearest-neighbor classifier that operates
in a PCA subspace.
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