3D face recognition with sparse spherical representations

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a fully automated registration process. They are then represented as signals on the 2-sphere in order to preserve depth and geometry informa...

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Vydané v:Pattern recognition Ročník 43; číslo 3; s. 824 - 834
Hlavní autori: Sala Llonch, R., Kokiopoulou, E., Tošić, I., Frossard, P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 01.03.2010
Elsevier
Predmet:
3D face recognition
Applied sciences
Dimensionality reduction
Exact sciences and technology
Image processing
Information, signal and communications theory
Pattern recognition
Signal processing
Sparse representations
Spherical representations
Telecommunications and information theory
Dimensionality reduction
Sparse representations
Spherical representations
3D face recognition
Biometrics
Performance evaluation
Discriminant analysis
Dimensionality
State of the art
Clouds
Face recognition
Image processing
Subspace method
Pattern recognition
Algorithm
Implementation
Automatic processing
Sparse representation
Tridimensional image
Automatic recognition
ISSN:0031-3203, 1873-5142
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Popis
Shrnutí:This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a fully automated registration process. They are then represented as signals on the 2-sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where linear discriminant analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2009.07.005