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115
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,
From Few to many: Illumination cone models for face recognition under variable lighting and pose
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a generative appearancebased method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a smal ..."
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Cited by 754 (12 self)
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We present a generative appearancebased method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render—or synthesize—images of the face under novel poses and illumination conditions. The pose space is then sampled, and for each pose the corresponding illumination cone is approximated by a lowdimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone (based on Euclidean distance within the image space). We test our face recognition method on 4050 images from the Yale Face Database B; these images contain 405 viewing conditions (9 poses ¢ 45 illumination conditions) for 10 individuals. The method performs almost without error, except on the most extreme lighting directions, and significantly outperforms popular recognition methods that do not use a generative model.
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
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Cited by 526 (20 self)
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We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a lowdimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions. Finally, we show a simple way to enforce nonnegative lighting when the images of an object lie near a 4D linear space. Research conducted w...
Acquiring linear subspaces for face recognition under variable lighting
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in ..."
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Cited by 317 (2 self)
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Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources, and again PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a lowdimensional linear space, and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.
The BasRelief Ambiguity
 IN THE PROCEEDINGS OF CVPR97.
, 1997
"... Since antiquity, artisans have created attened forms, often called "basreliefs," which give an exaggerated perception of depth when viewed from a particular vantage point. This paper presents an explanation of this phenomena, showing that the ambiguity in determining the relief of an obje ..."
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Cited by 168 (12 self)
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Since antiquity, artisans have created attened forms, often called "basreliefs," which give an exaggerated perception of depth when viewed from a particular vantage point. This paper presents an explanation of this phenomena, showing that the ambiguity in determining the relief of an object is not con ned to basrelief sculpture but is implicit in the determination of the structure of any object. Formally, if the object's true surface is denoted by z true = f(x � y), then we define the "generalized basrelief transformation" asz = f(x � y)+ x + y � with a corresponding transformation of the albedo. For each image of a Lambertian surface f(x � y) produced by a point light source at in nity, there exists an identical image of a basrelief produced by a transformed light source. This equality holds for both shaded and shadowed regions. Thus, the set of possible images (illumination cone) is invariant over generalized basrelief transformations. When = =0(e.g. a classical basrelief sculpture), we show that the set of possible motion elds are also identical. Thus, neither small unknown motions nor changes of illumination can resolve the basrelief ambiguity. Implications of this ambiguity on structure recovery and shape representation are discussed.
ThreeDimensional Face Recognition
, 2005
"... An expressioninvariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expressioninvariant representations of faces using the bendinginvariant canonical forms approach. The re ..."
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Cited by 150 (24 self)
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An expressioninvariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expressioninvariant representations of faces using the bendinginvariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expressioninvariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.
Subspace Linear Discriminant Analysis for Face Recognition
, 1999
"... In this paper we describe a holistic face recognition method based on subspace Linear Discriminant Analysis (LDA). The method consists of two steps: first we project the face image from the original vector space to a face subspace via Principal Component Analysis where the subspace dimension is care ..."
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Cited by 136 (8 self)
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In this paper we describe a holistic face recognition method based on subspace Linear Discriminant Analysis (LDA). The method consists of two steps: first we project the face image from the original vector space to a face subspace via Principal Component Analysis where the subspace dimension is carefully chosen, and then use LDA to obtain a linear classifier in the subspace. The criterion we use to choose the subspace dimension enables us to generate classseparable features via LDA. In addition, we employ a weighted distance metric guided by the LDA eigenvalues to improve the performance of the subspace LDA method. Finally, the improved performance of the subspace LDA approach is demonstrated through experiments using the FERET dataset for face recognition/verification, a large mugshot dataset for person verification, and the MPEG7 dataset. 1 Partially supported by the Office of Naval Research under Grant N000149510521. I. Introduction The problem of automatic face recognition...
Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2002
"... Lambertian object ..."
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From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
, 2000
"... Abstract Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearancebased methods which, unlike previous appearancebased approaches, require only a small set of training images to generate a rich representation that models this ..."
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Cited by 111 (8 self)
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Abstract Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearancebased methods which, unlike previous appearancebased approaches, require only a small set of training images to generate a rich representation that models this variability. Specifically, from as few as three images of an object in fixed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was specifically gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods. 1 Introduction An object can appear strikingly different due to changes in pose and illumination (see Figure 1). To handle this image variability, object recognition systems usually use one of the following approaches: (a) control viewing conditions, (b) employ a representation that is invariant to the viewing conditions, or (c) directly model this variability. For example, there is a long tradition of performing edge detection at an early stage since the presence of an edge at an image location is thought to be largely independent of lighting. It has been observed, however, that methods for face recognition based on finding local image features and using their geometric relation are generally ineffective [4].
Face recognition from a single image per person: A survey
 PATTERN RECOGNITION
, 2006
"... One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques ..."
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Cited by 108 (6 self)
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One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called “one sample per person ” problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.