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Face Detection Using Mixtures of Linear Subspaces (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Yang, Ming-Hsuan Kriegman, David Ahuja, Narendra |
| Abstract | 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 as the ... |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Different Expression Decision Rule Face Detection Linear Subspace Maximum Likelihood Fisher Linear Discriminant Input Sample Pattern Classification Face Detection Using Mixture Predefined Threshold Optimal Projection Gaussian Distribution Self-organizing Map Em Algorithm Face Image Gray Level Image Local Dimensionality Reduction Class-conditional Density Function Wide Range Different Lighting Condition Maximum Likelihood Estimate Mixture Model Different Pose Subspace Method Us Kohonen Factor Analyzer Projected Sample |
| Content Type | Text |
