Results 11  20
of
411
Matching 2.5D face scans to 3D models
 PATTERN ANALYSIS AND MACHINE INTELLIGENCE, IEEE TRANSACTIONS ON
, 2006
"... The performance of face recognition systems that use twodimensional images depends on factors such as lighting and subject’s pose. We are developing a face recognition system that utilizes threedimensional shape information to make the system more robust to arbitrary pose and lighting. For each s ..."
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Cited by 87 (4 self)
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The performance of face recognition systems that use twodimensional images depends on factors such as lighting and subject’s pose. We are developing a face recognition system that utilizes threedimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearancebased matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearancebased matching stage. Threedimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.
Orthogonal laplacianfaces for face recognition
 IEEE Trans. Image Process
, 2006
"... [30] V. Patrascu and V. Buzuloiu, “Image dynamic range enhancement in ..."
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Cited by 68 (3 self)
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[30] V. Patrascu and V. Buzuloiu, “Image dynamic range enhancement in
Sparse representation for signal classification
 In Adv. NIPS
, 2006
"... In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that incl ..."
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Cited by 64 (0 self)
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In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one that measures the signal reconstruction error and another that measures the sparsity. This objective function works well in applications where signals need to be reconstructed, like coding and denoising. On the other hand, discriminative methods, such as linear discriminative analysis (LDA), are better suited for classification tasks. However, discriminative methods are usually sensitive to corruption in signals due to lacking crucial properties for signal reconstruction. In this paper, we present a theoretical framework for signal classification with sparse representation. The approach combines the discrimination power of the discriminative methods with the reconstruction property and the sparsity of the sparse representation that enables one to deal with signal corruptions: noise, missing data and outliers. The proposed approach is therefore capable of robust classification with a sparse representation of signals. The theoretical results are demonstrated with signal classification tasks, showing that the proposed approach outperforms the standard discriminative methods and the standard sparse representation in the case of corrupted signals. 1
Structured Sparse Principal Component Analysis
, 2009
"... We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This structured sparse PCA is based on a structured regularization recently introduced by [1]. While ..."
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Cited by 57 (14 self)
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We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This structured sparse PCA is based on a structured regularization recently introduced by [1]. While classical sparse priors only deal with cardinality, the regularization we use encodes higherorder information about the data. We propose an efficient and simple optimization procedure to solve this problem. Experiments with two practical tasks, face recognition and the study of the dynamics of a protein complex, demonstrate the benefits of the proposed structured approach over unstructured approaches. 1
Subclass discriminant analysis
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2006
"... Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of highdimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand). Unfortunately, in most problem ..."
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Cited by 53 (10 self)
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Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of highdimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand). Unfortunately, in most problems the form of each class pdf is a priori unknown, and the selection of the DA algorithm that best fits our data is done over trialanderror. Ideally, one would like to have a single formulation which can be used for most distribution types. This can be achieved by approximating the underlying distribution of each class with a mixture of Gaussians. In this approach, the major problem to be addressed is that of determining the optimal number of Gaussians per class, i.e., the number of subclasses. In this paper, two criteria able to find the most convenient division of each class into a set of subclasses are derived. Extensive experimental results are shown using five databases. Comparisons are given against Linear Discriminant Analysis (LDA), Direct LDA (DLDA), Heteroscedastic LDA (HLDA), Nonparametric DA (NDA), and KernelBased LDA (KLDA). We show that our method is always the best or comparable to the best.
Graph embedding: a general framework for dimensionality reduction. CVPR
, 2005
"... In the last decades, a large family of algorithmsũ supervised or unsupervised; stemming from statistic or geometry theoryũhave been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a ..."
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Cited by 50 (8 self)
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In the last decades, a large family of algorithmsũ supervised or unsupervised; stemming from statistic or geometry theoryũhave been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a general framework, graph embedding along with its linearization and kernelization, which in theory reveals the underlying objective shared by most previous algorithms. It presents a unified perspective to understand these algorithms; that is, each algorithm can be considered as the direct graph embedding or its linear/kernel extension of some specific graph characterizing certain statistic or geometry property of a data set. Furthermore, this framework is a general platform to develop new algorithm for dimensionality reduction. To this end, we propose a new supervised algorithm, Marginal Fisher Analysis (MFA), for dimensionality reduction by designing two graphs that characterize the intraclass compactness and interclass separability, respectively. MFA measures the intraclass compactness with the distance between each data point and its neighboring points of the same class, and measures the interclass separability with the class margins; thus it overcomes the limitations of traditional Linear Discriminant Analysis algorithm in terms of data distribution assumptions and available projection directions. The toy problem on artificial data and the real face recognition experiments both show the superiority of our proposed MFA in comparison to LDA. 1.
Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble
 IEEE Transaction on Neural Networks
, 2005
"... Abstract—Most classical templatebased frontal face recognition techniques assume that multiple images per person are available for training, while in many realworld applications only one training image per person is available and the test images may be partially occluded or may vary in expressions ..."
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Cited by 50 (9 self)
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Abstract—Most classical templatebased frontal face recognition techniques assume that multiple images per person are available for training, while in many realworld applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local probabilistic approach presented by Martinez, using the SelfOrganizing Map (SOM) instead of a mixture of Gaussians to learn the subspace that represented each individual. Based on the localization of the training images, two strategies of learning the SOM topological space are proposed, namely to train a single SOM map for all the samples and to train a separate SOM map for each class, respectively. A soft k nearest neighbor (soft kNN) ensemble method, which can effectively exploit the outputs of the SOM topological space, is also proposed to identify the unlabelled subjects. Experiments show that the proposed method exhibits high robust performance against the partial occlusions and variant expressions. Index Terms—Face recognition, single training image per person, occlusion, face expression, selforganizing map I.
Optimal Linear Representations of Images for Object Recognition
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... Linear representations of images are commonly used in object recognition; however, frequently used ones (namely, PCA, ICA, and FDA) are generally far from optimal in terms of actual recognition performance. We propose a (MonteCarlo) simulated annealing algorithm that leads to optimal linear represe ..."
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Cited by 50 (12 self)
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Linear representations of images are commonly used in object recognition; however, frequently used ones (namely, PCA, ICA, and FDA) are generally far from optimal in terms of actual recognition performance. We propose a (MonteCarlo) simulated annealing algorithm that leads to optimal linear representations by maximizing the performance over subspaces. We illustrate its effectiveness using recognition experiments.
Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for ..."
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Cited by 44 (2 self)
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Abstract—Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for classification tasks, are highly sensitive to corrupted data. We present a theoretical framework for achieving the best of both types of methods: An approach that combines the discrimination power of discriminative methods with the reconstruction property of reconstructive methods which enables one to work on subsets of pixels in images to efficiently detect and reject the outliers. The proposed approach is therefore capable of robust classification with a highbreakdown point. We also show that subspace methods, such as CCA, which are used for solving regression tasks, can be treated in a similar manner. The theoretical results are demonstrated on several computer vision tasks showing that the proposed approach significantly outperforms the standard discriminative methods in the case of missing pixels and images containing occlusions and outliers. Index Terms—Subspace methods, reconstructive methods, discriminative methods, robust classification, robust regression,
Where are linear feature extraction methods applicable
 IEEE Trans. Pattern Anal. Mach. Intell
, 2005
"... Abstract—A fundamental problem in computer vision and pattern recognition is to determine where and, most importantly, why a given technique is applicable. This is not only necessary because it helps us decide which techniques to apply at each given time. Knowing why current algorithms cannot be app ..."
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Cited by 43 (15 self)
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Abstract—A fundamental problem in computer vision and pattern recognition is to determine where and, most importantly, why a given technique is applicable. This is not only necessary because it helps us decide which techniques to apply at each given time. Knowing why current algorithms cannot be applied facilitates the design of new algorithms robust to such problems. In this paper, we report on a theoretical study that demonstrates where and why generalized eigenbased linear equations do not work. In particular, we show that when the smallest angle between the ith eigenvector given by the metric to be maximized and the first i eigenvectors given by the metric to be minimized is close to zero, our results are not guaranteed to be correct. Several properties of such models are also presented. For illustration, we concentrate on the classical applications of classification and feature extraction. We also show how we can use our findings to design more robust algorithms. We conclude with a discussion on the broader impacts of our results. Index Terms—Feature extraction, generalized eigenvalue decomposition, performance evaluation, classifiers, pattern recognition. æ 1