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FGR
2000
IEEE

Face Detection Using Mixtures of Linear Subspaces

11 years 11 months ago
Face Detection Using Mixtures of Linear Subspaces
We present two methods using mixtures of linear subspaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using an EM algorithm. A face is detected if the probability of an input sample is above a predefined threshold. The other mixture of subspaces method uses Kohonen’s self-organizing map for clustering and Fisher Linear Discriminant to find the optimal projection for pattern classification, and a Gaussian distribution to model the class-conditional density function of the projected samples for each class. The parameters of the class-conditional density functions are maximum likelihood estimates and the decision rule is also based on maximum likelihood. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used a...
Ming-Hsuan Yang, Narendra Ahuja, David J. Kriegman
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where FGR
Authors Ming-Hsuan Yang, Narendra Ahuja, David J. Kriegman
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