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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 79 (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.
3D face recognition without facial surface reconstruction
, 2003
"... Recently, a 3D face recognition approach based on geometric invariant signatures, has been proposed. The key idea of the algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from facial expressions. One of the crucial stages in the constr ..."
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Cited by 4 (1 self)
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Recently, a 3D face recognition approach based on geometric invariant signatures, has been proposed. The key idea of the algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from facial expressions. One of the crucial stages in the construction of the geometric invariants is the measurement of geodesic distances on triangulated surfaces, carried out by fast marching on triangulated domains (FMTD). Proposed here is a method, which uses only the metric tensor of the surface for geodesic distance computation. When combined with photometric stereo used for facial surface acquisition, it allows constructing a bendinginvariant representation of the face without reconstructing the 3D surface.
On Bending Invariant Signatures for Surfaces
"... Abstract—Isometric surfaces share the same geometric structure, also known as the “first fundamental form. ” For example, all possible bendings of a given surface that includes all length preserving deformations without tearing or stretching the surface are considered to be isometric. We present a m ..."
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Cited by 4 (0 self)
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Abstract—Isometric surfaces share the same geometric structure, also known as the “first fundamental form. ” For example, all possible bendings of a given surface that includes all length preserving deformations without tearing or stretching the surface are considered to be isometric. We present a method to construct a bending invariant signature for such surfaces. This invariant representation is an embedding of the geometric structure of the surface in a small dimensional Euclidean space in which geodesic distances are approximated by Euclidean ones. The bending invariant representation is constructed by first measuring the intergeodesic distances between uniformly distributed points on the surface. Next, a multidimensional scaling (MDS) technique is applied to extract coordinates in a finite dimensional Euclidean space in which geodesic distances are replaced by Euclidean ones. Applying this transform to various surfaces with similar geodesic structures (first fundamental form) maps them into similar signature surfaces. We thereby translate the problem of matching nonrigid objects in various postures into a simpler problem of matching rigid objects. As an example, we show a simple surface classification method that uses our bending invariant signatures. Index Terms—MDS (MultiDimensional Scaling), FMTD (Fast Marching Method on Triangulate Domains), isometric signature, classification, geodesic distance.
Bending Invariant Representations for Surfaces
, 2001
"... Isometric surfaces share the same geometric structure also known as the `first fundamental form'. For example, all possible bending of a given surface, that include all length preserving deformations without tearing or stretching the surface, are considered to be isometric. We present a method to co ..."
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Isometric surfaces share the same geometric structure also known as the `first fundamental form'. For example, all possible bending of a given surface, that include all length preserving deformations without tearing or stretching the surface, are considered to be isometric. We present a method to construct a bending invariant canonical form for such surfaces. This invariant representation is an embedding of the intrinsic geodesic structure of the surface in a finite dimensional Euclidean space, in which geodesic distances are approximated by Euclidean ones. The canonical representation is constructed by first measuring the inter geodesic distances between points on the surfaces. Next, multidimensional scaling (MDS) techniques are applied to extract a finite dimensional flat space in which geodesic distances are represented as Euclidean ones. The geodesic distances are measured by the efficient `fast marching on triangulated domains' numerical algorithm. Applying this transform to various objects with similar geodesic structures (similar first fundamental form) maps isometric objects into similar canonical forms. We show a simple surface classification method based on the bending invariant canonical form.