Mixtures of local linear subspaces for face recognition

Brendan J. Frey, Antonio Colmenarez, Thomas S Huang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)32-37
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 1998

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Mixtures of local linear subspaces for face recognition'. Together they form a unique fingerprint.

Cite this