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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)
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)
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)
We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or " configuration " which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and(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)