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Face Recognition: A Literature Survey

by W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld , 2000
"... ... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into ..."
Abstract - Cited by 1398 (21 self) - Add to MetaCart
... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date 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,

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

by Aude Oliva, Antonio Torralba - International Journal of Computer Vision , 2001
"... In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a se ..."
Abstract - Cited by 1313 (81 self) - Add to MetaCart
In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.

Detecting faces in images: A survey

by Ming-hsuan Yang, David J. Kriegman, Narendra Ahuja - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2002
"... Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image se ..."
Abstract - Cited by 839 (4 self) - Add to MetaCart
Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
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Pictorial Structures for Object Recognition

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - IJCV , 2003
"... In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance ..."
Abstract - Cited by 816 (15 self) - Add to MetaCart
In this paper we present a statistical framework for modeling the appearance of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to model an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We use these models to address the problem of detecting an object in an image as well as the problem of learning an object model from training examples, and present efficient algorithms for both these problems. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.
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PCA versus LDA

by Aleix M. Martinez, Avinash C. Kak - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2001
"... In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (Linear Discriminant Analysis) are superior to those based on PCA (Principal Components Analysis) . In this communication we show that this is not always the case. We present ..."
Abstract - Cited by 472 (16 self) - Add to MetaCart
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (Linear Discriminant Analysis) are superior to those based on PCA (Principal Components Analysis) . In this communication we show that this is not always the case. We present our case first by using intuitively plausible arguments and then by showing actual results on a face database. Our overall conclusion is that when the training dataset is small, PCA can outperform LDA, and also that PCA is less sensitive to different training datasets. Keywords: face recognition, pattern recognition, principal components analysis, linear discriminant analysis, learning from undersampled distributions, small training datasets.

Locality Preserving Projection,"

by Xiaofei He , Partha Niyogi - Neural Information Processing System, , 2004
"... Abstract Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the ..."
Abstract - Cited by 414 (16 self) - Add to MetaCart
Abstract Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) -a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.

Face recognition using laplacianfaces

by Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-jiang Zhang - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005
"... Abstract—We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) wh ..."
Abstract - Cited by 389 (38 self) - Add to MetaCart
Abstract—We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Index Terms—Face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, face manifold, subspace learning. 1
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Face recognition by elastic bunch graph matching,

by Laurenz Wiskott , ‡ , Jean-Marc Fellous , § , Norbert Krüger , ¶ , Christoph Von Der Malsburg - IEEE Trans. Patt. Anal. Mach. Intell. , 1997
"... Abstract We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise fa ..."
Abstract - Cited by 367 (9 self) - Add to MetaCart
Abstract We present a system for recognizing human faces from single images out of a large database containing one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth, etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straightforward comparison of image graphs. We report recognition experiments on the FERET database as well as the Bochum database, including recognition across pose.
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Statistical Models of Appearance for Computer Vision

by T.F. Cootes, C.J. Taylor , 2000
"... ..."
Abstract - Cited by 357 (2 self) - Add to MetaCart
Abstract not found

Context-Based Vision System for Place and Object Recognition

by Antonio Torralba, Kevin P. Murphy, William T. Freeman, Mark Rubin , 2003
"... While navigating in an environment, a vision system has' to be able to recognize where it is' and what the main objects' in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is' to identify familiar locations' (e ..."
Abstract - Cited by 317 (9 self) - Add to MetaCart
While navigating in an environment, a vision system has' to be able to recognize where it is' and what the main objects' in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is' to identify familiar locations' (e.g., office 610, conference room 941, Main Street), to categorize new environments' (office, corridor, street) and to use that information to provide contextualpriors for object recognition (e.g., table, chair, car, computeD. We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors' that simplify object recognition. We have trained the system to recognize over 60 locations (indoors' and outdoors') and to suggest the presence and locations' of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user. 1This work was sponsored by the Air Force under Air Force Contract F19628-00-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the U.S. Government.
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