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...

• Type: Conference
• Year: 2000
• Where: FGR
• Authors: Ming-Hsuan Yang, Narendra Ahuja, David J. Kriegman
• Keywords: Biometrics, Class-conditional Density Functions, FGR 2000, Maximum Likelihood, Perform Local Dimensionality