TY - CHAP
T1 - Face recognition from facial surface metric
AU - Bronstein, Alexander M.
AU - Bronstein, Michael M.
AU - Spira, Alon
AU - Kimmel, Ron
PY - 2004
Y1 - 2004
N2 - Recently, a 3D face recognition approacli 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 cost-efficient 3D acquisition techniques such as photometric stereo. Avoiding the explicit surface reconstruction stage saves computational time and reduces numerical errors.
AB - Recently, a 3D face recognition approacli 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 cost-efficient 3D acquisition techniques such as photometric stereo. Avoiding the explicit surface reconstruction stage saves computational time and reduces numerical errors.
UR - http://www.scopus.com/inward/record.url?scp=35048893012&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-24671-8_18
DO - 10.1007/978-3-540-24671-8_18
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AN - SCOPUS:35048893012
SN - 3540219838
SN - 9783540219835
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 237
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Pajdla, Tomáš
A2 - Matas, Jiří
PB - Springer Verlag
ER -