Multiple images can be indexed by their projections on a few \eigenfeatures". These eigenfeatures are the eigenvectors of a large covariance matrix, constructed from the images. It is known that registration is essential for computing \useful" eigenfeatures, and a preliminary step of putting the images in register is common practice. We propose to evaluate multiple image registration by the qua...

Principal Components Analysis (PCA) is often used as a feature extraction procedure. Given a matrix X ∈ R, whose rows represent n data points with respect to d features, the top k right singular vectors of X (the so-called eigenfeatures), are arbitrary linear combinations of all available features. The eigenfeatures are very useful in data analysis, including the regularization of linear regres...

Large collections of images can be indexed by projections on a few “eigenfeatures”, the dominant eigenvectors of the images covariance matrix. A preliminary step of registering the images is common practice. A quantitative analysis of what is being gained by registration was not performed in previous work, and heuristics were used to determine on what to register the images. We show that the re...

This paper presents a novel view-based approach to quantify and reproduce facial expressions, by systematically exploiting the degrees of freedom allowed by a realistic face model. This approach embeds e cient mesh morphing and texture animations to synthesize facial expressions. We suggest using eigenfeatures, built from synthetic images, and designing an estimator to interpret the responses o...

We present a novel view{based approach to quantify and reproduce facial expressions, by systematically exploiting the degrees of freedom allowed by a realistic face model, which embeds e cient mesh morphing and texture animations to synthesize facial expressions. For this purpose, we propose to use eigenfeatures, built from synthetic images, and design a linear estimator to interpret the respon...

This paper presents a novel algorithm for the extraction of the eye and mouth (facial features) "elds from 2-D gray-level face images. The fundamental philosophy is that eigenfeatures, derived from the eigenvalues and eigenvectors of the binary edge data set constructed from the eye and mouth "elds, are very good features to locate these "elds e$ciently. The eigenfeatures extracted from the pos...

This paper presents a video coding techique that achieves high visual quality at very low bit rates. Each video frame is divided into two regions, consisiting of a background area and a visually important feature to be coded at higher bit rates. The feature is tracked from frame to frame and it is coded using a set of features that are extracted from a training set. The set of features, which w...

|This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the eeectiveness of these Most Discriminating Features for view-based class retrieval from a large database of widely varying real-world objects presented as \well-framed" views, and...

We describe a face modeling system which estimates complete facial structure and texture from a real-time video stream. The system begins with a face tracking algorithm which detects and stabilizes live facial images into a canonical 3D pose. The resulting canonical texture is then processed by a statistical model to l-ter imperfections and estimate unknown components such as missing pixels and...

We present a novel face feature extraction approach using localized Principal Component Analysis (PCA) learning in face recognition tasks. The localized PCA approach produces a set of fine-tuned feature specific masks from a constrained subset of the input distribution. This method is a guided-learning based on a set of pre-defined feature points over a short training sequence. The result is th...