Results 1  10
of
225
Face Recognition: A Literature Survey
, 2000
"... ... This paper provides an uptodate critical survey of still and videobased face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an uptodate review of the existing literature, and the second is to offer some insights into ..."
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Cited by 1398 (21 self)
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... This paper provides an uptodate critical survey of still and videobased face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an uptodate review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition,
Weighted lowrank approximations.
 In Int. Conf. Machine Learning (ICML),
, 2003
"... Abstract We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted lowrank approximation problems, which, unlike their unweighted version, do not admit a closedform solution in general. We analyze ..."
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Cited by 198 (10 self)
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Abstract We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving weighted lowrank approximation problems, which, unlike their unweighted version, do not admit a closedform solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility of accommodating the weights in reconstructing the underlying lowrank representation, and extend the formulation to nonGaussian noise models such as logistic models. Finally, we apply the methods developed to a collaborative filtering task.
Multilinear Analysis of Image Ensembles: TensorFaces
 IN PROCEEDINGS OF THE EUROPEAN CONFERENCE ON COMPUTER VISION
, 2002
"... Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. Multilinear algebra, the algebra of higherorder tensors, offers a potent mathematical framework for analyzing the multifactor structure of image ensembles and for addressing the d ..."
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Cited by 188 (7 self)
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Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. Multilinear algebra, the algebra of higherorder tensors, offers a potent mathematical framework for analyzing the multifactor structure of image ensembles and for addressing the difficult problem of disentangling the constituent factors or modes. Our multilinear modeling technique employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the Nmode SVD.As a concrete example, we consider the multilinear analysis of ensembles of facial images that combine several modes, including different facial geometries (people), expressions, head poses, and lighting conditions. Our resulting "TensorFaces" representation has several advantages over conventional eigenfaces. More generally, multilinear analysis shows promise as a unifying framework for a variety of computer vision problems.
Incremental Singular Value Decomposition Of Uncertain Data With Missing Values
 IN ECCV
, 2002
"... We introduce an incremental singular value decomposition (SVD) of incomplete data. The SVD is developed as data arrives, and can handle arbitrary missing/untrusted values, correlated uncertainty across rows or columns of the measurement matrix, and user priors. Since incomplete data does not uniq ..."
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Cited by 179 (5 self)
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We introduce an incremental singular value decomposition (SVD) of incomplete data. The SVD is developed as data arrives, and can handle arbitrary missing/untrusted values, correlated uncertainty across rows or columns of the measurement matrix, and user priors. Since incomplete data does not uniquely specify an SVD, the procedure selects one having minimal rank. For a dense p q matrix of low rank r, the incremental method has time complexity O(pqr) and space complexity O((p + q)r)better than highly optimized batch algorithms such as MATLAB 's svd(). In cases of missing data, it produces factorings of lower rank and residual than batch SVD algorithms applied to standard missingdata imputations. We show applications in computer vision and audio feature extraction. In computer vision, we use the incremental SVD to develop an efficient and unusually robust subspaceestimating flowbased tracker, and to handle occlusions/missing points in structurefrommotion factorizations.
Learning Distance Functions Using Equivalence Relations
 In Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... We address the problem of learning distance metrics using sideinformation in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and e#cient algorithm for learning a full ranked Mahalanobis metric (Shental et al., 2002). ..."
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Cited by 173 (6 self)
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We address the problem of learning distance metrics using sideinformation in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and e#cient algorithm for learning a full ranked Mahalanobis metric (Shental et al., 2002).
Multilinear Subspace Analysis of Image Ensembles
 PROCEEDINGS OF 2003 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
, 2003
"... Multilinear algebra, the algebra of higherorder tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear ..."
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Cited by 119 (2 self)
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Multilinear algebra, the algebra of higherorder tensors, offers a potent mathematical framework for analyzing ensembles of images resulting from the interaction of any number of underlying factors. We present a dimensionality reduction algorithm that enables subspace analysis within the multilinear framework. This Nmode orthogonal iteration algorithm is based on a tensor decomposition known as the Nmode SVD, the natural extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the power of multilinear subspace analysis in the context of facial image ensembles, where the relevant factors include different faces, expressions, viewpoints, and illuminations. In prior work we showed that our multilinear representation, called TensorFaces, yields superior facial recognition rates relative to standard, linear (PCA/eigenfaces) approaches. Here, we demonstrate factorspecific dimensionality reduction of facial image ensembles. For example, we can suppress illumination effects (shadows, highlights) while preserving detailed facial features, yielding a low perceptual error.
Describing visual scenes using transformed dirichlet processes
 Advances in Neural Information Processing Systems 18
, 2005
"... Motivated by the problem of learning to detect and recognize objects with minimal supervision, we develop a hierarchical probabilistic model for the spatial structure of visual scenes. In contrast with most existing models, our approach captures the intrinsic uncertainty in the number and identity o ..."
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Cited by 89 (7 self)
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Motivated by the problem of learning to detect and recognize objects with minimal supervision, we develop a hierarchical probabilistic model for the spatial structure of visual scenes. In contrast with most existing models, our approach captures the intrinsic uncertainty in the number and identity of objects depicted in a given image. Our scene model is based on the transformed Dirichlet process (TDP), a novel extension of the hierarchical DP in which a set of stochastically transformed mixture components are shared between multiple groups of data. For visual scenes, mixture components describe the spatial structure of visual features in an object–centered coordinate frame, while transformations model the object positions in a particular image. Learning and inference in the TDP, which has many potential applications beyond computer vision, is based on an empirically effective Gibbs sampler. Applied to a dataset of partially labeled street scenes, we show that the TDP’s inclusion of spatial structure improves detection performance, and allows unsupervised discovery of object categories. 1
Style translation for human motion
 ACM Transactions on Graphics
, 2005
"... Figure 1: Our style translation system transforms a normal walk (TOP) into a sneaky crouch (MIDDLE) and a sideways shuffle (BOTTOM). Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of inte ..."
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Cited by 88 (1 self)
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Figure 1: Our style translation system transforms a normal walk (TOP) into a sneaky crouch (MIDDLE) and a sideways shuffle (BOTTOM). Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions and a linear timeinvariant model to represent stylistic differences. Once the model is estimated with system identification, the system is capable of translating streaming input with simple linear operations at each frame.
Learning to Represent Spatial Transformations with Factored HigherOrder Boltzmann Machines
, 2010
"... To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced threeway multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric we ..."
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Cited by 75 (18 self)
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To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced threeway multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a threedimensional interaction tensor. We describe a lowrank approximation to this interaction tensor that uses a sum of factors, each of which is a threeway outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.
Learning with Matrix Factorization
, 2004
"... Matrices that can be factored into a product of two simpler matrices can serve as a useful and often natural model in the analysis of tabulated or highdimensional data. Models based on matrix factorization (Factor Analysis, PCA) have been extensively used in statistical analysis and machine learning ..."
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Cited by 71 (6 self)
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Matrices that can be factored into a product of two simpler matrices can serve as a useful and often natural model in the analysis of tabulated or highdimensional data. Models based on matrix factorization (Factor Analysis, PCA) have been extensively used in statistical analysis and machine learning for over a century, with many new formulations and models suggested in recent