Robust Parameterized Component Analysis

Principal ComponentAnalysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the variation in the appearance of people's faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we develop person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in previous work, this has involved a time consuming and errorprone hand alignment and cropping process. Instead, we introduce parameterized component analysis to learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques ac...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Fernando De la Torre, Michael J. Black
• Keywords: Computer Vision, ECCV 2002, Facial Appearance Models, Facial Expressions, Facial Modeling, Intra-person Appearance Changes, Modular Pca