Results 1  10
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25
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 113 (22 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.
Expressioninvariant 3D face recognition
, 2003
"... We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the ..."
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Cited by 92 (17 self)
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We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These signature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms.
Efficient Computation of IsometryInvariant Distances between Surfaces
"... We present an efficient computational framework for isometryinvariant comparison of smooth surfaces. We formulate the GromovHausdorff distance as a multidimensional scaling (MDS)like continuous optimization problem. In order to construct an efficient optimization scheme, we develop a numerical ..."
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Cited by 55 (18 self)
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We present an efficient computational framework for isometryinvariant comparison of smooth surfaces. We formulate the GromovHausdorff distance as a multidimensional scaling (MDS)like continuous optimization problem. In order to construct an efficient optimization scheme, we develop a numerical tool for interpolating geodesic distances on a sampled surface from precomputed geodesic distances between the samples. For isometryinvariant comparison of surfaces in the case of partially missing data, we present the partial embedding distance, which is computed using a similar scheme. The main idea is finding a minimumdistortion mapping from one surface to another, while considering only relevant geodesic distances. We discuss numerical implementation issues and present experimental results that demonstrate its accuracy and efficiency.
Calculus of nonrigid surfaces for geometry and texture manipulation
 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 2007
"... We present a geometric framework for automatically finding intrinsic correspondence between threedimensional nonrigid objects. We model object deformation as near isometries and find the correspondence as the minimumdistortion mapping. A generalization of multidimensional scaling is used as the n ..."
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Cited by 28 (10 self)
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We present a geometric framework for automatically finding intrinsic correspondence between threedimensional nonrigid objects. We model object deformation as near isometries and find the correspondence as the minimumdistortion mapping. A generalization of multidimensional scaling is used as the numerical core of our approach. As a result, we obtain the possibility to manipulate the extrinsic geometry and the texture of the objects as vectors in a linear space. We demonstrate our method on the problems of expressioninvariant texture mapping onto an animated threedimensional face, expression exaggeration, morphing between faces, and virtual body painting.
ExpressionInvariant Representations of Faces
 IEEE TRANS. PAMI
, 2007
"... Addressed here is the problem of constructing and analyzing expressioninvariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the constru ..."
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Cited by 26 (6 self)
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Addressed here is the problem of constructing and analyzing expressioninvariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the construction of expressioninvariant representation of a face involves embedding of the facial intrinsic geometric structure into some convenient lowdimensional space. We study the influence of the embedding space geometry and dimensionality choice on the representation accuracy and argue that compared to its Euclidean counterpart, spherical embedding leads to notably smaller metric distortions. We experimentally support our claim showing that a smaller embedding error leads to better recognition.
Geodesic Object Representation and Recognition
 IN PROCEEDINGS OF DGCI, VOLUME LNCS
, 2003
"... This paper describes a shape signature that captures the intrinsic geometric structure of 3D objects. The primary motivation of the proposed approach is to encode a 3D shape into a onedimensional geodesic distribution function. This compact and computationally simple representation is based on ..."
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Cited by 24 (2 self)
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This paper describes a shape signature that captures the intrinsic geometric structure of 3D objects. The primary motivation of the proposed approach is to encode a 3D shape into a onedimensional geodesic distribution function. This compact and computationally simple representation is based on a global geodesic distance defined on the object surface, and takes the form of a kernel density estimate. To gain further insight into the geodesic shape distribution and its practicality in 3D computer imagery, some numerical experiments are provided to demonstrate the potential and the much improved performance of the proposed methodology in 3D object matching. This is carried out using an informationtheoretic measure of dissimilarity between probabilistic shape distributions.
Robust expressioninvariant face recognition from partially missing data
 IN PROC. ECCV. 2006, LECTURE NOTES ON COMPUTER SCIENCE
, 2006
"... Recent studies on threedimensional face recognition proposed to model facial expressions as isometries of the facial surface. Based on this model, expressioninvariant signatures of the face were constructed by means of approximate isometric embedding into flat spaces. Here, we apply a new method ..."
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Cited by 22 (6 self)
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Recent studies on threedimensional face recognition proposed to model facial expressions as isometries of the facial surface. Based on this model, expressioninvariant signatures of the face were constructed by means of approximate isometric embedding into flat spaces. Here, we apply a new method for measuring isometryinvariant similarity between faces by embedding one facial surface into another. We demonstrate that our approach has several significant advantages, one of which is the ability to handle partially missing data. Promising face recognition results are obtained in numerical experiments even when the facial surfaces are severely occluded.
SpectralDriven IsometryInvariant Matching of 3D Shapes
, 2009
"... This paper presents a matching method for 3D shapes, which comprises a new technique for surface sampling and two algorithms for matching 3D shapes based on pointbased statistical shape descriptors. Our sampling technique is based on critical points of the eigenfunctions related to the smaller eige ..."
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Cited by 15 (1 self)
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This paper presents a matching method for 3D shapes, which comprises a new technique for surface sampling and two algorithms for matching 3D shapes based on pointbased statistical shape descriptors. Our sampling technique is based on critical points of the eigenfunctions related to the smaller eigenvalues of the LaplaceBeltrami operator. These critical points are invariant to isometries and are used as anchor points of a sampling technique, which extends the farthest point sampling by using statistical criteria for controlling the density and number of reference points. Once a set of reference points has been computed, for each of them we construct a pointbased statistical descriptor (PSSD, for short) of the input surface. This descriptor incorporates an approximation of the geodesic shape distribution and other geometric information describing the surface at that point. Then, the dissimilarity between two surfaces is computed by comparing the corresponding sets of PSSDs with bipartite graph matching or measuring the L1distance between the reordered feature vectors of a proximity graph. Here, the reordering is given by the Fiedler vector of a Laplacian matrix
Face recognition from facial surface metric
 IN PROC. ECCV
, 2004
"... Recently, a 3D face recognition approach based on geometric invariant signatures, has been proposed. The key idea is a representation of the facial surface, invariant to isometric deformations, such as those resulting from facial expressions. One important stage in the construction of the geometric ..."
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Cited by 12 (5 self)
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Recently, a 3D face recognition approach based on geometric invariant signatures, has been proposed. The key idea is a representation of the facial surface, invariant to isometric deformations, such as those resulting from facial expressions. One important stage in the construction of the geometric invariants involves in measuring geodesic distances on triangulated surfaces, which is carried out by the fast marching on triangulated domains algorithm. Proposed here is a method that uses only the metric tensor of the surface for geodesic distance computation. That is, the explicit integration of the surface in 3D from its gradients is not needed for the recognition task. It enables the use of simple and costefficient 3D acquisition techniques such as photometric stereo. Avoiding the explicit surface reconstruction stage saves computational time and reduces numerical errors.