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—We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These(More)
ÐNonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low-resolution ªthumbnailº faces processed from 1,755 images from the FERET face database. The performance of SVMs (3.4 percent error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant,(More)
In this work we describe experiments with eigen-faces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10 3) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in(More)
We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image di!erences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching(More)
We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance(More)
This paper presents progress toward an integrated, robust , real-time face detection and demographic analysis system. Faces are detected and extracted using the fast algorithm recently proposed by Viola and Jones [16]. Detected faces are passed to a demographics classifier which uses the same architecture as the face detector. This demographic classifier is(More)
Support Vector Machines (SVMs) are investigated for visual gender classification with low resolution " thumbnail " faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor)(More)
We propose a novel technique for direct visual matching of images for the purposes of face recognition and database search. Speciically, we argue in favor of a probabilistic measure of similarity, in contrast to simpler methods which are based on standard L2 norms (e.g., template matching) or subspace-restricted norms (e.g., eigenspace matching). The(More)
Sparse PCA seeks approximate sparse " eigenvectors " whose projections capture the maximal variance of data. As a cardinality-constrained and non-convex optimization problem, it is NP-hard and is encountered in a wide range of applied fields, from bio-informatics to finance. Recent progress has focused mainly on continuous approximation and convex(More)