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Probabilistic Visual Learning for Object Representation
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 derivedExpand
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View-based and modular eigenspaces for face recognition
We describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10/sup 3/) faces. TheExpand
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Learning Gender with Support Faces
Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognitionExpand
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Visual tracking and recognition using appearance-adaptive models in particle filters
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 andExpand
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Bayesian face recognition
Abstract 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 aExpand
  • 633
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Probabilistic visual learning for object detection
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 derivedExpand
  • 461
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  • PDF
A unified learning framework for real time face detection and classification
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 proposed by P. Viola andExpand
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Principal Manifolds and Probabilistic Subspaces for Visual Recognition
  • B. Moghaddam
  • Computer Science
  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 1 June 2002
Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques - principal component analysis (PCA),Expand
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Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms
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 isExpand
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Gender classification with support vector machines
Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. TheExpand
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