Three-Dimensional Face Recognition

By A. Bronstein, Michael M. Bronstein, Ron Kimmel. International Journal of Computer Vision, 2005

475 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/62492438300fb5c6247fc314f31eca2eb737854f

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Shape Analysis of Elastic Curves in Euclidean Spaces (2011, background): “a facial surface as the shortest-path distance from the tip of the nose (similar to [3] and [21]), (2) defines facial curves to be the level curves of that function, and (3) represents the shapes of facial surfaces using indexed collections of their facial curves.”
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network (2016, background): “Indeed, previous work showed that when available, high resolution 3D face scans are excellent face representations which can even be used to distinguish between the faces of identical twins [9].”
One-Shot Person Re-identification with a Consumer Depth Camera (2014, background): “([7], [8]) tackle this problem by applying an isometric embedding which allows to get rid of pose variability (extrinsic geometry) by warping shapes to a canonical form where geodesic distances are replaced by Euclidean ones.”
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition (2007, background): “Bronstein et al. [ 8 ] proposed an expression-invariant multimodal face recognition algorithm.”
Gromov–Wasserstein Distances and the Metric Approach to Object Matching (2011, methodology): “The practical procedure of [16] is the same as that of [37]: given a collection of points A sampled from a surface, they computed the matrix DA of inter-point geodesic distances.”
Partial Similarity of Objects, or How to Compare a Centaur to a Horse (2009, background): “In the face recognition community, extensive research has been done on similarities insensitive to illumination [50, 11], head pose [42], and facial expressions [12, 13].”
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition (2006, background): “[11] present an approach to 3D face recognition intended to allow for deformation related to facial expression.”
A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching (2010, methodology): “This method has been used in three-dimensional face recognition ( Bronstein et al. 2005 ), analysis of articulated two-dimensional shapes and images (Ling and Jacobs 2005a, 2005b), texture mapping and object morphing (Bronstein et al. 2007a; Grossman et al. 2002 ;Z igelman et al.2002), and shape seg”
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression (2006, background): “A fourth approach is to try to compute an expression-invariant representation of the 3D face shape [11], [12].”
Expression invariant 3D face recognition with a Morphable Model (2008, background): “Another interesting model-less approach [ 7 ] compares surfaces by the distribution of geodesics, which stays constant for nonrigidly deforming (but not stretching or tearing) objects.”